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 4151–4175 of 8378 papers

TitleStatusHype
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Conditional Adversarial Synthesis of 3D Facial Action Units—0
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network—0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images—0
Concurrent Adversarial Learning for Large-Batch Training—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
Feature Space Transfer for Data Augmentation—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech—0
Learning Visual Representations with Optimum-Path Forest and its Applications to Barrett's Esophagus and Adenocarcinoma Diagnosis—0
A general framework for defining and optimizing robustness—0
Feature Matching Data Synthesis for Non-IID Federated Learning—0
Feature-level Malware Obfuscation in Deep Learning—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Computational Ceramicology—0
Assessment Framework for Deepfake Detection in Real-world Situations—0
Feature-based Style Randomization for Domain Generalization—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