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 2051–2075 of 8378 papers

TitleStatusHype
A Survey on Neural Architecture Search—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives—0
Dependent Relational Gamma Process Models for Longitudinal Networks—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Learning from Pairwise Measurements—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
A Survey on Face Data Augmentation—0
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Contrastive Learning for Low Resource Machine Translation—0
Implicit Rugosity Regularization via Data Augmentation—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
State Classification of Cooking Objects Using a VGG CNN—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
A Survey on Deep Clustering: From the Prior Perspective—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
A Car Model Identification System for Streamlining the Automobile Sales Process—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
A Survey on Data Synthesis and Augmentation for Large Language Models—0
ContraGAN: Contrastive Learning for Conditional Image Generation—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