SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

Further readings:

( Image credit: Albumentations )

Papers

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Showing 3626–3650 of 8378 papers

TitleStatusHype
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
GAN-based Data Augmentation for Chest X-ray Classification—0
Generative Adversarial Networks for Data Augmentation—0
Contrastive Visual Data Augmentation—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples—0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks—0
GAMMA: Generative Augmentation for Attentive Marine Debris Detection—0
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation—0
Generative AI in Industrial Machine Vision -- A Review—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Contrastive Self-supervised Learning for Graph Classification—0
Asymptotically exact data augmentation: models, properties and algorithms—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
Pedestrian Attribute Editing for Gait Recognition and Anonymization—0
Generative Cooperative Net for Image Generation and Data Augmentation—0
Gait Data Augmentation using Physics-Based Biomechanical Simulation—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement—0
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection—0
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified