Result of 10-Fold cross-validation on augmented dataset.

The goal of this work is to generate large statistically representative datasets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student-t process regression. We apply Student-t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via coloring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics, and classic machine learning clustering algorithms.

The Vertebral Augmentation Market Report is Segmented by Product (Vertebroplastic Device and Kyphoplastic Device), End User (Hospitals, Ambulatory Surgery Centers, and Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The Report Offers the Value (in USD) for the Above Segments.

Data Augmentation Tools Market Outlook

As per our latest research, the global Data Augmentation Tools market size reached USD 1.47 billion in 2024, reflecting the rapidly increasing adoption of artificial intelligence and machine learning across diverse sectors. The market is experiencing robust momentum, registering a CAGR of 25.3% from 2025 to 2033. By the end of 2033, the Data Augmentation Tools market is forecasted to reach a substantial value of USD 11.6 billion. This impressive growth is primarily driven by the escalating need for high-quality, diverse datasets to train advanced AI models, coupled with the proliferation of digital transformation initiatives across industries.

The primary growth factor fueling the Data Augmentation Tools market is the exponential rise in AI and machine learning applications, which require vast amounts of labeled data for effective training. As organizations strive to develop more accurate and robust models, the demand for data augmentation solutions that can synthetically expand and diversify datasets has surged. This trend is particularly pronounced in sectors such as healthcare, automotive, and retail, where the quality and quantity of data directly impact the performance and reliability of AI systems. The market is further propelled by the increasing complexity of data types, including images, text, audio, and video, necessitating sophisticated augmentation tools capable of handling multimodal data.

Another significant driver is the growing focus on reducing model bias and improving generalization capabilities. Data augmentation tools enable organizations to generate synthetic samples that account for various real-world scenarios, thereby minimizing overfitting and enhancing the robustness of AI models. This capability is critical in regulated industries like BFSI and healthcare, where the consequences of biased or inaccurate models can be severe. Furthermore, the rise of edge computing and IoT devices has expanded the scope of data augmentation, as organizations seek to deploy AI solutions in resource-constrained environments that require optimized and diverse training datasets.

The proliferation of cloud-based solutions has also played a pivotal role in shaping the trajectory of the Data Augmentation Tools market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations of all sizes to access advanced augmentation capabilities without significant infrastructure investments. Additionally, the integration of data augmentation tools with popular machine learning frameworks and platforms has streamlined adoption, enabling seamless workflow integration and accelerating time-to-market for AI-driven products and services. These factors collectively contribute to the sustained growth and dynamism of the global Data Augmentation Tools market.

From a regional perspective, North America currently dominates the Data Augmentation Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and early adoption of digital transformation initiatives have established North America as a key hub for data augmentation innovation. Meanwhile, Asia Pacific is poised for the fastest growth over the forecast period, driven by the rapid expansion of the IT and telecommunications sector, burgeoning e-commerce industry, and increasing government initiatives to promote AI adoption. Europe also maintains a significant market presence, supported by stringent data privacy regulations and a strong focus on ethical AI development.

Component Analysis

The Component segment of the Data Augmentation Tools market is bifurcated into Software and Services, each playing a critical role in enabling organizations to leverage data augmentation for AI and machine learning initiatives. The software sub-segment comprises

Data Augmentation Tools Market Outlook

According to our latest research, the global Data Augmentation Tools market size reached USD 1.62 billion in 2024, with a robust year-on-year growth trajectory. The market is poised for accelerated expansion, projected to achieve a CAGR of 26.4% from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 12.34 billion. This dynamic growth is primarily driven by the rising demand for artificial intelligence (AI) and machine learning (ML) applications across diverse industry verticals, which necessitate vast quantities of high-quality training data. The proliferation of data-centric AI models and the increasing complexity of real-world datasets are compelling enterprises to invest in advanced data augmentation tools to enhance data diversity and model robustness, as per the latest research insights.

One of the principal growth factors fueling the Data Augmentation Tools market is the intensifying adoption of AI-driven solutions across industries such as healthcare, automotive, retail, and finance. Organizations are increasingly leveraging data augmentation to overcome the challenges posed by limited or imbalanced datasets, which are often a bottleneck in developing accurate and reliable AI models. By synthetically expanding training datasets through augmentation techniques, enterprises can significantly improve the generalization capabilities of their models, leading to enhanced performance and reduced risk of overfitting. Furthermore, the surge in computer vision, natural language processing, and speech recognition applications is creating a fertile environment for the adoption of specialized augmentation tools tailored to image, text, and audio data.

Another significant factor contributing to market growth is the rapid evolution of augmentation technologies themselves. Innovations such as Generative Adversarial Networks (GANs), automated data labeling, and domain-specific augmentation pipelines are making it easier for organizations to deploy and scale data augmentation strategies. These advancements are not only reducing the manual effort and expertise required but also enabling the generation of highly realistic synthetic data that closely mimics real-world scenarios. As a result, businesses across sectors are able to accelerate their AI/ML development cycles, reduce costs associated with data collection and labeling, and maintain compliance with stringent data privacy regulations by minimizing the need to use sensitive real-world data.

The growing integration of data augmentation tools within cloud-based AI development platforms is also acting as a major catalyst for market expansion. Cloud deployment offers unparalleled scalability, accessibility, and collaboration capabilities, allowing organizations of all sizes to harness the power of data augmentation without significant upfront infrastructure investments. This democratization of advanced data engineering tools is especially beneficial for small and medium enterprises (SMEs) and academic research institutes, which often face resource constraints. The proliferation of cloud-native augmentation solutions is further supported by strategic partnerships between technology vendors and cloud service providers, driving broader market penetration and innovation.

From a regional perspective, North America continues to dominate the Data Augmentation Tools market, driven by the presence of leading AI technology companies, a mature digital infrastructure, and substantial investments in research and development. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digital transformation initiatives, a burgeoning startup ecosystem, and increasing government support for AI innovation. Europe also holds a significant share, underpinned by strong regulatory frameworks and a focus on ethical AI development. Meanwhile, Latin America and the Middle East & Africa are witnessing steady adoption, particularly in sectors such as BFSI and healthcare, where data-driven insights are becoming increasingly critical.

Component Analysis

The Data Augmentation Tools market by component is bifurcated into Software and Services. The software segment currently accounts for the largest share of the market, owing to the widespread deployment of standalone and integrated augmentation solutions across enterprises and research institutions. These software plat

Analysis of factors for association with the presence of augmentation.

The size of the Human Augmentation market was valued at USD 81580 million in 2024 and is projected to reach USD 277310.32 million by 2033, with an expected CAGR of 19.1% during the forecast period.

Comparison of accuracy between the proposed system and existing systems.

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 human augmentation technology market is projected to grow from USD 10,840.0 million in 2023 to USD 28,653.6 million by 2030, at a CAGR of 13.6% during the forecast period. The market is driven by factors such as increasing demand for wearable devices, growing adoption of augmented reality and virtual reality (AR/VR) devices, and rising healthcare expenditure. Key trends in the market include:

The increasing development and adoption of wearable devices, such as smartwatches and fitness trackers, is driving the growth of the body-worn segment. The growing adoption of AR/VR devices in various industries, such as manufacturing, healthcare, and education, is fueling the growth of the non-body-worn segment. The rising demand for advanced healthcare and medical devices is driving the growth of the medical segment. The growing adoption of industrial automation and robotics is driving the growth of the industrial segment. The increasing demand for advanced defense and security technologies is driving the growth of the defense segment.

The size of the Human Augmentation market was valued at USD 68300 million in 2024 and is projected to reach USD 221469.50 million by 2033, with an expected CAGR of 18.3 % during the forecast period.

The global buttock augmentation market was valued at USD 1.53 billion in 2020 and is expected to grow at a CAGR of 22.1% during 2021 - 2028.

Description:

This dataset contains augmented images of six different dermatological conditions. Each category includes 399 images, providing a balanced dataset ideal for training machine learning models, particularly in the field of medical image analysis.

Categories:

1. Acne: A common skin condition that occurs when hair follicles become clogged with oil and dead skin cells, leading to pimples, blackheads, or whiteheads.
2. Carcinoma: A type of skin cancer that begins in the basal or squamous cells. The images in this category may show various stages and forms of skin carcinoma.
3. Eczema: A condition that makes the skin red, inflamed, itchy, and sometimes results in blisters. The images depict different manifestations of eczema.
4. Keratosis: A skin condition characterized by rough, scaly patches on the skin caused by excessive growth of keratin. This category includes images of various types of keratosis, such as actinic keratosis.
5. Milia: Small, white, benign bumps that typically appear on the face, especially around the eyes and on the cheeks. The images show different instances of this condition.
6. Rosacea: A chronic skin condition that causes redness and visible blood vessels in your face. This category contains images depicting the typical characteristics of rosacea.

Dataset Details:

Total Images: 2,394 Images per Category: 399 Image Format: JPEG Image Size: Variable. Augmentation Techniques: The images have been augmented using techniques such as rotation, flipping, zooming, and brightness adjustment to enhance the diversity of the dataset and improve model generalization.

This entry contains input files used for the analysis from the paper "Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation" by Andrew G Duncan, Jennifer A Mitchell, and Alan M Moses. The code to run the analysis can be found at "https://github.com/agduncan94/phylogenetic_augmentation_paper"

The growing preference for minimally invasive procedures and advancements in filler technology propel this market’s growth. With an estimated valuation of USD 1.4 billion by 2035 and a CAGR of 4.3%.

Attributes	Key Insights
Market Value, 2035	USD 1.4 billion
Value CAGR (2025 to 2035)	4.3%

Vendor Performance

Category	Industry Share (%)
top 3 (Allergan, Revance, Galderma)	60-75%
rest of top 5 (Sinclair Pharma plc. , Teoxane Laboratories Inc.)	10-15%
Others	15-20%

Regional Analysis

Region	Market Share (%)
North America	40%
Europe	25%
Asia-Pacific	20%
Rest of the World	15%

Tier-Wise Vendor Classification

Tier	Tier 1
Market Share (%)	65%
Key Companies	Allergan, Revance, Galderma

Tier	Tier 2
Market Share (%)	17%
Key Companies	Medytox, HUGEL, Laboratoires Filorga

Tier	Tier 3
Market Share (%)	12%
Key Companies	Regional and niche players

The global human augmentation market was valued at USD 130.71 billion in 2021 and is expected to grow at a CAGR of 22.0% during the forecast period.

Abstract: The integration of neural-network-based systems into clinical practice is limited by challenges related to domain generalization and robustness. The computer vision community established benchmarks such as ImageNet-C as a fundamental prerequisite to measure progress towards those challenges. Similar datasets are largely absent in the medical imaging community which lacks a comprehensive benchmark that spans across imaging modalities and applications. To address this gap, we create and open-source MedMNIST-C, a benchmark dataset based on the MedMNIST+ collection, covering 12 datasets and 9 imaging modalities. We simulate task and modality-specific image corruptions of varying severity to comprehensively evaluate the robustness of established algorithms against real-world artifacts and distribution shifts. We further provide quantitative evidence that our simple-to-use artificial corruptions allow for highly performant, lightweight data augmentation to enhance model robustness. Unlike traditional, generic augmentation strategies, our approach leverages domain knowledge, exhibiting significantly higher robustness when compared to widely adopted methods. By introducing MedMNIST-C and open-sourcing the corresponding library allowing for targeted data augmentations, we contribute to the development of increasingly robust methods tailored to the challenges of medical imaging. The code is available at github.com/francescodisalvo05/medmnistc-api.

This work has been accepted at the Workshop on Advancing Data Solutions in Medical Imaging AI @ MICCAI 2024 [preprint].

Note: Due to space constraints, we have uploaded all datasets except TissueMNIST-C. However, it can be reproduced via our APIs.

Usage: We recommend using the demo code and tutorials available on our GitHub repository.

Citation: If you find this work useful, please consider citing us:

@article{disalvo2024medmnist,
 title={MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions},
 author={Di Salvo, Francesco and Doerrich, Sebastian and Ledig, Christian},
 journal={arXiv preprint arXiv:2406.17536},
 year={2024}
}

Disclaimer: This repository is inspired by MedMNIST APIs and the ImageNet-C repository. Thus, please also consider citing MedMNIST, the respective source datasets (described here), and ImageNet-C.

Check Market Research Intellect's Biological Augmentation Services Market Report, pegged at USD 750 million in 2024 and projected to reach USD 1.5 billion by 2033, advancing with a CAGR of 8.5% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

The size of the Human Augmentation Technology market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

Biogeographic history of Malesian RhododendronBiogeographic history of Malesian Rhododendron. The region was parsed into 20 discrete geographic areas following Brown et al. (2006). Each circle corresponds to a discrete area whose geographic coordinates is summarized by its location on the map. Posterior probability of being present in an area is proportional to the opacity of the circle, with the data at the tips being observed with probability one. Circles are colored according to their position relative to Wallace’s Line. We infer a continental Asian origin for Malesian rhododendrons. This figure complements the summarized results presented in FIgure 7 in the manuscript and is best explored using zoom.supp_vireya_map.pdfVireya phylogenyTime-calibrated phylogeny of Rhododendron section Vireya from Webb & Ree (2012).malaysia.55.treeVireya biogeographical coordinatesGeographical coordinates used to represent biogeographical areas of Rhododendron section Vireya as defined by Brown et ...