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.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5401–5425 of 8378 papers

TitleStatusHype
Adversarial AutoAugment—0
Adversarial Backdoor Defense in CLIP—0
Adversarial Bone Length Attack on Action Recognition—0
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification—0
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation—0
Adversarial Data Augmentation for Disordered Speech Recognition—0
Adversarial Data Augmentation for Robust Speaker Verification—0
Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition—0
Adversarial Data Augmentation via Deformation Statistics—0
Adversarial Diversity and Hard Positive Generation—0
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization—0
Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models—0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise—0
Adversarial Learning for Neural PDE Solvers with Sparse Data—0
Adversarially Optimized Mixup for Robust Classification—0
Adversarial Policy Optimization in Deep Reinforcement Learning—0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records—0
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes—0
Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data—0
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified