Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

The BRA-Dataset is an expanded dataset of Brazilian wildlife, developed for object detection tasks, combining real images with synthetic samples generated by Generative Adversarial Networks (GANs). It includes five medium- and large-sized mammal species frequently involved in roadkill incidents on Brazilian highways: lowland tapir (Tapirus terrestris), jaguarundi (Herpailurus yagouaroundi), maned wolf (Chrysocyon brachyurus), puma (Puma concolor), and giant anteater (Myrmecophaga tridactyla). The primary goal is to provide a comprehensive and standardized resource for biodiversity conservation research, wildlife monitoring technologies, and computer vision applications, with an emphasis on automated wildlife detection.

The original dataset by Ferrante et al. (2022) was built from images of wildlife captured through camera traps, field cameras, and structured internet searches, followed by manual curation and bounding box annotation. In this work, the dataset was expanded to approximately 9,238 images, divided into three main groups:

1. Real images — original photographs collected from the aforementioned sources. Total: 1,823.

2. Images augmented by classical techniques — generated from real images using transformations such as rotations (RT), horizontal flips (HF), vertical flips (VF), and horizontal (HS) and vertical shifts (VS). Total: 7,300.

3. Synthetic images generated by GANs — produced with WGAN-GP models trained individually for each species, using pre-processed image subsets. All generated samples underwent qualitative assessment to ensure morphological consistency, proper framing, and visual fidelity before inclusion. Total: 115.

The directory structure is organized into images/ and labels/, each subdivided into train/ and val/, following an 80% training and 20% validation split. Images are provided in .jpg format and annotations in .txt following the YOLO standard (class_id x_center y_center width height, with normalized coordinates). Furthermore, the file naming convention is designed to clearly indicate the species and the type of data augmentation applied.

The dataset is compatible with various object detection architectures and was evaluated using YOLOv5, YOLOv8, and YOLOv11 in n, s, and m variants, aiming to assess the impact of dataset expansion in scenarios with different computational capabilities and performance requirements.

By combining real data, classical augmentations, and high-quality synthetic samples, the BRA-Dataset provides a valuable resource for wildlife detection, environmental monitoring, and conservation research, especially in contexts where image availability for rare or threatened species is limited.

Identifying small molecules that bind strongly to target proteins in rational molecular design is crucial. Machine learning techniques, such as generative adversarial networks (GAN), are now essential tools for generating such molecules. In this study, we present an enhanced method for molecule generation using objective-reinforced GANs. Specifically, we introduce BEGAN (Boltzmann-enhanced GAN), a novel approach that adjusts molecule occurrence frequencies during training based on the Boltzmann distribution exp(−ΔU/τ), where ΔU represents the estimated binding free energy derived from docking algorithms and τ is a temperature-related scaling hyperparameter. This Boltzmann reweighting process shifts the generation process toward molecules with higher binding affinities, allowing the GAN to explore molecular spaces with superior binding properties. The reweighting process can also be refined through multiple iterations without altering the overall distribution shape. To validate our approach, we apply it to the design of sex pheromone analogs targeting Spodoptera frugiperda pheromone receptor SfruOR16, illustrating that the Boltzmann reweighting significantly increases the likelihood of generating promising sex pheromone analogs with improved binding affinities to SfruOR16, further supported by atomistic molecular dynamics simulations. Furthermore, we conduct a comprehensive investigation into parameter dependencies and propose a reasonable range for the hyperparameter τ. Our method offers a promising approach for optimizing molecular generation for enhanced protein binding, potentially increasing the efficiency of molecular discovery pipelines.

Criteria for detailed characterization of the dataset.

AI in Generative Adversarial Networks (GANs) Market Outlook

According to our latest research, the global AI in Generative Adversarial Networks (GANs) market size reached USD 2.65 billion in 2024, reflecting robust growth driven by rapid advancements in deep learning and artificial intelligence. The market is expected to register a remarkable CAGR of 31.4% from 2025 to 2033, accelerating the adoption of GANs across diverse industries. By 2033, the market is forecasted to achieve a value of USD 32.78 billion, underscoring the transformative impact of GANs in areas such as image and video generation, data augmentation, and synthetic content creation. This trajectory is supported by the increasing demand for highly realistic synthetic data and the expansion of AI-driven applications across enterprise and consumer domains.

A primary growth factor for the AI in Generative Adversarial Networks market is the exponential increase in the availability and complexity of data that organizations must process. GANs, with their unique adversarial training methodology, have proven exceptionally effective for generating realistic synthetic data, which is crucial for industries like healthcare, automotive, and finance where data privacy and scarcity are significant concerns. The ability of GANs to create high-fidelity images, videos, and even text has enabled organizations to enhance their AI models, improve data diversity, and reduce bias, thereby accelerating the adoption of AI-driven solutions. Furthermore, the integration of GANs with cloud-based platforms and the proliferation of open-source GAN frameworks have democratized access to this technology, enabling both large enterprises and SMEs to harness its potential for innovative applications.

Another significant driver for the AI in Generative Adversarial Networks market is the surge in demand for advanced content creation tools in media, entertainment, and marketing. GANs have revolutionized the way digital content is produced by enabling hyper-realistic image and video synthesis, deepfake generation, and automated design. This has not only streamlined creative workflows but also opened new avenues for personalized content, virtual influencers, and immersive experiences in gaming and advertising. The rapid evolution of GAN architectures, such as StyleGAN and CycleGAN, has further enhanced the quality and scalability of generative models, making them indispensable for enterprises seeking to differentiate their digital offerings and engage customers more effectively in a highly competitive landscape.

The ongoing advancements in hardware acceleration and AI infrastructure have also played a pivotal role in propelling the AI in Generative Adversarial Networks market forward. The availability of powerful GPUs, TPUs, and AI-specific chips has significantly reduced the training time and computational costs associated with GANs, making them more accessible for real-time and large-scale applications. Additionally, the growing ecosystem of AI services and consulting has enabled organizations to overcome technical barriers, optimize GAN deployments, and ensure compliance with evolving regulatory standards. As investment in AI research continues to surge, the GANs market is poised for sustained innovation and broader adoption across sectors such as healthcare diagnostics, autonomous vehicles, financial modeling, and beyond.

From a regional perspective, North America continues to dominate the AI in Generative Adversarial Networks market, accounting for the largest share in 2024, driven by its robust R&D ecosystem, strong presence of leading technology companies, and early adoption of AI technologies. Europe follows closely, with significant investments in AI research and regulatory initiatives promoting ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, expanding AI talent pool, and increasing government support for AI innovation. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as enterprises in these regions begin to explore the potential of GANs for industry-specific applications.

Component Analysis

The AI in Generative Adversarial Networks market is segmented by component into software, hardware, and services, each playing a vital role in the ecosystem’s development and adoption. Software solutions constitute the largest share of the market in 2024, reflecting the growing demand for ad

Artificial Intelligence-based image generation has recently seen remarkable advancements, largely driven by deep learning techniques, such as Generative Adversarial Networks (GANs). With the influx and development of generative models, so too have biometric re-identification models and presentation attack detection models seen a surge in discriminative performance. However, despite the impressive photo-realism of generated samples and the additive value to the data augmentation pipeline, the role and usage of machine learning models has received intense scrutiny and criticism, especially in the context of biometrics, often being labeled as untrustworthy. Problems that have garnered attention in modern machine learning include: humans' and machines' shared inability to verify the authenticity of (biometric) data, the inadvertent leaking of private biometric data through the image synthesis process, and racial bias in facial recognition algorithms. Given the arrival of these unwanted side effects, public trust has been shaken in the blind use and ubiquity of machine learning.

However, in tandem with the advancement of generative AI, there are research efforts to re-establish trust in generative and discriminative machine learning models. Explainability methods based on aggregate model salience maps can elucidate the inner workings of a detection model, establishing trust in a post hoc manner. The CYBORG training strategy, originally proposed by Boyd, attempts to actively build trust into discriminative models by incorporating human salience into the training process.

In doing so, CYBORG-trained machine learning models behave more similar to human annotators and generalize well to unseen types of synthetic data. Work in this dissertation also attempts to renew trust in generative models by training generative models on synthetic data in order to avoid identity leakage in models trained on authentic data. In this way, the privacy of individuals whose biometric data was seen during training is not compromised through the image synthesis procedure. Future development of privacy-aware image generation techniques will hopefully achieve the same degree of biometric utility in generative models with added guarantees of trustworthiness.

Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning. In this study, we assessed whether a data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The stepwise augmentation of data comprised image rotation as well as visual and virtual augmentation. The visual data augmentation applies classic approaches of data augmentation and generation of artificial images using a Generative Adversarial Networks (GAN) approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. Finally, data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on four different image datasets, which include scarab beetle genitalia (Pleophylla, Schizonycha) as well as wing patterns of bees (Osmia) and cattleheart butterflies (Parides), our augmentation approach outperformed a deep learning baseline approach by means of resulting identification accuracy with non-augmented data as well as a traditional 2D morphometric approach (Procrustes analysis of scarab beetle genitalia).

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

Biomedical image analysis, data augmentation, Generative Adversarial Networks (GANs), synthetic images

Examples of EA selection rules (positive results).

Most facial expression recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques has been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems. To advance in this respect, we evaluate the impact of the GAN techniques by creating a new DB containing the generated synthetic images.

The face images contained in the KDEF DB serve as the basis for creating novel synthetic images by combining the facial features of two images (i.e., Candie Kung and Cristina Saralegui) selected from the YouTube-Faces DB. The novel images differ from each other, in particular concerning the eyes, the nose, and the mouth, whose characteristics are taken from the Candie and Cristina images.

The total number of novel synthetic images generated with the GAN is 980 (70 individuals from KDEF DB x 7 emotions x 2 subjects from YouTube-Faces DB).

The zip file "GAN_KDEF_Candie" contains the 490 images generated by combining the KDEF images with the Candie Kung image. The zip file "GAN_KDEF_Cristina" contains the 490 images generated by combining the KDEF images with the Cristina Saralegui image. The used image IDs are the same used for the KDEF DB. The synthetic generated images have a resolution of 562x762 pixels.

If you make use of this dataset, please consider citing the following publication:

Porcu, S., Floris, A., & Atzori, L. (2020). Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems. Electronics, 9, 1892, doi: 10.3390/electronics9111892, url: https://www.mdpi.com/2079-9292/9/11/1892.

BibTex format:

@article{porcu2020evaluation, title={Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems}, author={Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Electronics}, volume={9}, pages={108781}, year={2020}, number = {11}, article-number = {1892}, publisher={MDPI}, doi={10.3390/electronics9111892} }

We explore methods for data augmentation in neuroimaging. Specifically, we investigate the use of 3D Transfontanellar Ultrasound (3D US) for augmenting 2D datasets of neonatal neuroimages, and we also synthesize an artificial dataset of images using Generative Adversarial Networks (GANs).

This dataset consists on 2D slices of 3D US of the brain of neonates which has been successfully used to train an unconditional GAN for generating 2D US images as described in the presentation with DOI: 10.5281/zenodo.14917011

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global synthetic data generation engine market size reached USD 1.48 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for privacy-compliant data and advanced analytics solutions. The market is projected to grow at a remarkable CAGR of 35.6% from 2025 to 2033, reaching an estimated USD 18.67 billion by the end of the forecast period. This rapid growth is primarily propelled by the adoption of artificial intelligence (AI) and machine learning (ML) across various industry verticals, along with the escalating need for high-quality, diverse datasets that do not compromise sensitive information.

One of the primary growth factors fueling the synthetic data generation engine market is the heightened focus on data privacy and regulatory compliance. With stringent regulations such as GDPR, CCPA, and HIPAA being enforced globally, organizations are increasingly seeking solutions that enable them to generate and utilize data without exposing real customer information. Synthetic data generation engines provide a powerful means to create realistic, anonymized datasets that retain the statistical properties of original data, thus supporting robust analytics and model development while ensuring compliance with data protection laws. This capability is especially critical for sectors like healthcare, banking, and government, where data sensitivity is paramount.

Another significant driver is the surging adoption of AI and ML models across industries, which require vast volumes of diverse and representative data for training and validation. Traditional data collection methods often fall short due to limitations in data availability, quality, or privacy concerns. Synthetic data generation engines address these challenges by enabling the creation of customized datasets tailored for specific use cases, including rare-event modeling, edge-case scenario testing, and data augmentation. This not only accelerates innovation but also reduces the time and cost associated with data acquisition and labeling, making it a strategic asset for organizations seeking to maintain a competitive edge in AI-driven markets.

Moreover, the increasing integration of synthetic data generation engines into enterprise IT ecosystems is being catalyzed by advancements in cloud computing and scalable software architectures. Cloud-based deployment models are making these solutions more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. The flexibility to generate, store, and manage synthetic datasets in the cloud enhances collaboration, speeds up development cycles, and supports global operations. As a result, cloud adoption is expected to further accelerate market growth, particularly among businesses undergoing digital transformation and seeking to leverage synthetic data for innovation and compliance.

Regionally, North America currently dominates the synthetic data generation engine market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. North America's leadership is attributed to the presence of major technology providers, robust regulatory frameworks, and a high level of AI adoption across industries. Europe is experiencing rapid growth due to strong data privacy regulations and a thriving technology ecosystem, while Asia Pacific is emerging as a lucrative market, driven by digitalization initiatives and increasing investments in AI and analytics. The regional outlook suggests that market expansion will be broad-based, with significant opportunities for vendors and stakeholders across all major geographies.

Component Analysis

The component segment of the synthetic data generation engine market is bifurcated into software and services, each playing a vital role in the overall ecosystem. Software solutions form the backbone of this market, providing the core algorithms and platforms that enable the generation, management, and deployment of synthetic datasets. These platforms are continually evolving, integrating advanced techniques such as generative adversarial networks (GANs), variational autoencoders, and other deep learning models to produce highly realistic and diverse synthetic data. The software segment is anticipated to maintain its dominance throughout the forecast period, as organizations increasingly invest in proprietary and commercial tools to address their un

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.

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AI Creativity And Art Generation Market Size 2025-2029

The AI creativity and art generation market size is forecast to increase by USD 9.01 billion at a CAGR of 11.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the democratization of content creation and increased accessibility to advanced AI technologies. This trend is enabling a wider range of individuals and organizations to generate creative content, leading to new opportunities and applications in various industries. Mobility solutions and quantum computing are also expected to provide new growth opportunities. Furthermore, the ascendancy of multimodal and video generation is transforming the creative landscape, offering innovative solutions for marketing, entertainment, and education.
Additionally, ethical considerations surrounding the use of AI in art generation, such as authenticity and human creativity, necessitate ongoing dialogue and industry standards. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of emerging trends and engage in open discussions with stakeholders. However, the market faces challenges, including pervasive intellectual property and ethical dilemmas. Machine learning and 3D object detection are emerging trends in the market.

What will be the Size of the AI Creativity And Art Generation Market during the forecast period?

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In the dynamic market, digital art platforms are leveraging transformer-based models and image upscaling algorithms to enhance art creation techniques. Computer vision, a subset of artificial intelligence (AI), is revolutionizing various industries by enabling machines to identify and interpret visual information. Computer vision algorithms and image editing software enable image-to-image transformation, while stylegan2 architecture and GAN image generation push the boundaries of image synthesis. Convolutional neural networks and autoencoder compression optimize the image pipeline, and latent space manipulation, diffusion model sampling, and self-attention mechanisms fuel creative AI pipelines.

Recurrent image generation, image inpainting methods, text-guided image generation, and neural style transfer are also trending, as the AI art community explores new ways to manipulate and generate captivating visuals. The integration of these advanced techniques into art generation workflows is revolutionizing the way businesses approach AI image manipulation. As AI-generated content becomes more sophisticated, it raises questions about ownership and authorship, requiring clear guidelines and regulations. Machine learning and deep learning models are powering cloud and edge computing technologies, enhancing autonomous driving solutions in the automotive sector.

How is this AI Creativity And Art Generation Industry segmented?

The AI creativity and art generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Generative AI tools
  AI design tools
  AI music tools
  AI video and animation tools
  Others


Application

  Visual arts
  Music and sound design
  Film and animation
  Digital media and advertising
  Others


End-user

  Entertainment and media
  Marketing and advertising
  Gaming and VR
  Education and training
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    Singapore
    South Korea


  Rest of World (ROW)

By Type Insights

The Generative AI tools segment is estimated to witness significant growth during the forecast period. The text-to-image synthesis segment in the AI creativity market is experiencing significant advancements, driven by innovative technologies such as variational autoencoders, transformer networks, and generative adversarial networks. Edge computing is another crucial aspect of AI-driven predictive maintenance, enabling data processing at the source for quicker response times and improved efficiency. These tools enable digital art creation by generating novel images from user-defined prompts. Notable entities include computer vision techniques, attention mechanisms, super-resolution models, and recurrent neural networks. Model training efficiency and image generation pipelines are crucial factors, with diffusion models and data augmentation strategies employed to enhance performance. Loss functions optimization and backpropagation algorithms facilitate the refinement of these models.

Hyperparameter tuning and inpainting algorithms are essential fo

IntroductionThe challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions.MethodsIn this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications.ResultsQuantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN’s average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint’s FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61).DiscussionThis study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.

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AI Image Generator Market Size 2025-2029

The AI image generator market size is forecast to increase by USD 2.39 billion at a CAGR of 31.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the accelerated pace of technological innovation and model sophistication. This technological advancement has democratized content creation, enabling businesses and individuals to generate personalized visuals at scale. However, the market also faces challenges. Intellectual property uncertainty and widespread copyright disputes pose significant obstacles, requiring careful navigation to avoid potential legal issues. Semantic reasoning and predictive analytics are transforming decision making, while AI-powered chatbots and virtual assistants enhance customer service.
By focusing on innovation and intellectual property protection strategies, businesses can differentiate themselves and thrive in this dynamic market. Companies seeking to capitalize on market opportunities must stay abreast of technological advancements while addressing these challenges effectively. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives.

What will be the Size of the AI Image Generator Market during the forecast period?

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The market for AI image generators continues to evolve, with advancements in various areas such as cloud computing platforms, interactive image editing, image upscaling, and more. Technologies like batch normalization layers, adversarial training, noise injection methods, and neural network architectures are driving innovation in this space. For instance, the use of generative adversarial networks (GANs) has led to significant improvements in image fidelity and sample diversity metrics. Quantum computing and cognitive computing are emerging trends, offering faster processing power and advanced reasoning capabilities.

According to recent industry reports, the market is expected to grow by over 20% annually, driven by the increasing demand for real-time image generation, model deployment strategies, and API integration methods. One notable example of this trend is a leading e-commerce platform reporting a 30% increase in sales through the implementation of AI-powered image restoration and style mixing techniques. AI technologies, such as machine learning (ML), deep learning (DL), computer vision, speech recognition, and natural language processing, are transforming industries.

How is this AI Image Generator Industry segmented?

The AI image generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Application

  Marketing and advertising
  Gaming and entertainment
  E-commerce
  Fashion and design
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The Software segment is estimated to witness significant growth during the forecast period. The market's software component is experiencing rapid evolution, with a focus on photorealism, coherence, and advanced features. Generative models, applications, plugins, and APIs enable visual content creation from textual or other inputs. Since early 2023, there's been intense competition for superior performance, deeper ecosystem integration, and broader accessibility. Open-source software like SDXL, which offers access to advanced technology, prevents market monopolization and empowers customized solutions. Image quality assessment, data augmentation strategies, and image super-resolution are crucial components of generative model architectures. Conditional GANs, diffusion models, and semantic image editing are transforming the industry, with neural style transfer and model interpretability methods enhancing user experience.

Loss functions optimization, reinforcement learning, and generative adversarial networks are driving efficiency and innovation. Transformer networks, text-to-image generation, and image-to-image translation are revolutionizing the field. Attention mechanisms, feature extraction methods, and latent diffusion models are essential for advanced image manipulation techniques. Unsupervised learning and supervised learning, along with variational autoencoders and image inpainting, are expanding the market's potential applications. The industry is expected to grow by over 25% annually,

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AI In Ultrasound Imaging Market Size 2025-2029

The AI in ultrasound imaging market size is forecast to increase by USD 848.2 million, at a CAGR of 29.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the surging demand for enhanced diagnostic accuracy and workflow efficiency. The integration of Artificial Intelligence (AI) in ultrasound imaging is revolutionizing the industry, with AI-driven automation and workflow optimization becoming increasingly prevalent. This technological advancement offers substantial benefits, including improved image quality, faster analysis, and reduced human error. However, the market's strategic landscape is not without challenges. Generative AI, a subset of artificial intelligence, has the ability to create new and unique content, making it an invaluable tool in ultrasound imaging.
Regulatory bodies are increasingly scrutinizing AI applications in healthcare, necessitating stringent compliance. Moreover, securing adequate reimbursement for AI-enabled ultrasound imaging services remains a challenge. Companies seeking to capitalize on market opportunities must stay abreast of these regulatory developments and effectively address reimbursement challenges to ensure long-term success. Virtual reality (VR) and user interface (UI) innovations offer engaging experiences for medical professionals. Navigating the complex and evolving regulatory and reimbursement landscape poses a significant obstacle for market participants.

What will be the Size of the AI In Ultrasound Imaging Market during the forecast period?

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The ultrasound imaging market continues to evolve, driven by advancements in artificial intelligence (AI) technologies. Radiology workflows are being revolutionized through AI-driven ultrasound image enhancement, enabling quantitative ultrasound imaging and tissue characterization. Convolutional neural networks, computer vision techniques, and machine learning models are increasingly being employed for diagnostic accuracy improvement. For instance, a leading research institution reported a 20% increase in diagnostic accuracy using AI-assisted diagnosis and predictive modeling in contrast-enhanced ultrasound. Moreover, pattern recognition systems and AI-powered image segmentation are transforming medical image analysis, leading to a 15% growth expectation in the industry.
Ultrasound data annotation, deep learning algorithms, and generative adversarial networks are paving the way for automated lesion detection and 3D ultrasound reconstruction. Real-time image processing, image guided biopsy, and recurrent neural networks are further enhancing the capabilities of ultrasound technology. AI-driven image classification and data augmentation techniques are facilitating clinical decision support, while image registration methods and ultrasound-guided therapy are revolutionizing patient care. Image-based phenotyping and image quality assessment are also gaining traction, with workflow optimization and feature extraction methods further enhancing the overall efficiency of ultrasound imaging.

How is this AI In Ultrasound Imaging Industry segmented?

The AI in ultrasound imaging industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services
  Hardware


End-user

  Hospitals
  Diagnostic imaging centers
  Others


Application

  Neurology
  Radiology
  Obstetrics and gynecology
  Cardiovascular
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The Software segment is estimated to witness significant growth during the forecast period. The market is witnessing significant growth, with the software segment leading the way. This segment includes advanced algorithms, platforms, and integrated workflow solutions that analyze imaging data, automate measurements, and offer clinical decision support. The shift in value from the physical ultrasound device to the sophisticated software is driven by the development of sophisticated machine learning models, particularly deep learning. Trained on extensive, curated datasets, these models excel in tasks such as anomaly detection, anatomical segmentation, tissue characterization, and hemodynamic quantification, surpassing human capabilities in terms of speed and consistency. For instance, a recent study published in the Journal of Medical Imaging revealed that a deep learning model achieved a 94% diagnostic accuracy in identifying liver lesions, surpassing the performance of radiologists.

Moreover, the market

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Artificial Intelligence (AI) Market Size 2025-2029

The artificial intelligence market size is forecast to increase by USD 369.1 billion, at a CAGR of 34.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing need to prevent fraud and malicious attacks. Businesses are recognizing the value of AI in detecting and mitigating cyber threats, leading to increased adoption. Another key trend is the shift towards cloud-based AI services, offering scalability, flexibility, and cost savings. However, the market faces challenges, including the shortage of AI experts. As the demand for AI skills continues to rise, companies are finding it difficult to recruit and retain talent.
This talent crunch could hinder the growth of the AI market, necessitating innovative solutions such as upskilling current employees or partnering with external experts. To capitalize on the market's opportunities and navigate challenges effectively, companies must focus on developing robust AI strategies, investing in talent development, and collaborating with industry partners.

What will be the Size of the Artificial Intelligence (AI) Market during the forecast period?

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The market continues to evolve at an unprecedented pace, with cloud-based platforms becoming the norm for businesses seeking to leverage advanced AI capabilities. Deep learning models, fueled by semantic web technologies, are revolutionizing predictive analytics, enabling more accurate forecasting and pattern recognition. However, the integration of AI comes with ethical considerations, necessitating the development of bias mitigation strategies and Explainable Ai techniques. Moreover, large language models are transforming natural language processing, while knowledge graphs facilitate the efficient organization and retrieval of information. Model evaluation metrics are crucial for assessing the performance of various machine learning algorithms, from neural network architectures to decision support systems.

Time series analysis and Anomaly Detection are essential applications of AI in various sectors, including finance and manufacturing. For instance, a leading retailer reported a 15% increase in sales by implementing AI-powered automation and cognitive computing. The industry growth in AI is projected to reach 20% annually, with Federated Learning, hyperparameter optimization, and reinforcement learning being key areas of focus. Additionally, deep learning models are being employed in computer vision systems, speech recognition systems, risk assessment models, data mining algorithms, and data augmentation techniques. Generative adversarial networks and transfer learning methods are revolutionizing image processing techniques, while predictive analytics and pattern recognition are transforming various industries, from healthcare to transportation.

Despite the numerous benefits, AI deployment comes with challenges, such as the need for model training pipelines and the ethical implications of bias and privacy concerns. Nonetheless, ongoing research and innovation in AI ethics considerations, model evaluation metrics, and explainable AI techniques are addressing these challenges, ensuring the continued unfolding of market activities and evolving patterns.

How is this Artificial Intelligence (AI) Industry segmented?

The artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Hardware
  Services


End-user

  Retail
  Banking
  Manufacturing
  Healthcare
  Others


Technology

  Deep learning
  Machine learning
  NLP
  Gen AI


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

In the dynamic technology landscape, Artificial Intelligence (AI) continues to be a game-changer for businesses. Cloud-based AI platforms enable developers to build intelligent applications, integrating machine learning algorithms, deep learning models, and natural language processing. Ethical considerations are at the forefront, as semantic web technologies and knowledge graphs facilitate more harmonious human-AI interactions. Predictive analytics, powered by large language models and pattern recognition, offer valuable insights for decision-making. Transfer learning methods and federated learning enable AI systems to learn from diverse data sources, while bias mitigation strategies ensure fairness. Hyperparameter optimization and neural network architectures optimi

Examples of sentence transformation, Balakrishnan et al.