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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3651–3675 of 8378 papers

TitleStatusHype
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
RoentGen: Vision-Language Foundation Model for Chest X-ray GenerationCode1
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive LearningCode1
Mitigating and Evaluating Static Bias of Action Representations in the Background and the ForegroundCode1
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence ModelingCode1
Join the High Accuracy Club on ImageNet with A Binary Neural Network TicketCode1
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Fed-TDA: Federated Tabular Data Augmentation on Non-IID DataCode1
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition—0
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
Improving Crowded Object Detection via Copy-Paste—0
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted DataCode1
ModelDiff: A Framework for Comparing Learning AlgorithmsCode1
EVNet: An Explainable Deep Network for Dimension ReductionCode1
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models—0
RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural NetworkCode1
Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge—0
SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing DeepfakesCode1
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Background-Mixed Augmentation for Weakly Supervised Change DetectionCode1
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Coarse-to-fine Task-driven Inpainting for Geoscience Images—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Show:102550
← PrevPage 147 of 336Next →
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