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 5951–6000 of 8378 papers

TitleStatusHype
Autoregressive Latent Video Prediction with High-Fidelity Image Generator—0
Understanding Overfitting in Reweighting Algorithms for Worst-group Performance—0
Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models—0
Approximate Bijective Correspondence for isolating factors of variationCode0
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations—0
Where is the bottleneck in long-tailed classification?—0
Neuro-Symbolic Ontology-Mediated Query Answering—0
Segmentation of Roads in Satellite Images using specially modified U-Net CNNs—0
Latent Feature Disentanglement For Visual Domain Generalization—0
Sample-specific and Context-aware Augmentation for Long Tail Image Classification—0
SketchODE: Learning neural sketch representation in continuous time—0
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning—0
Improving Discriminative Visual Representation Learning via Automatic Mixup—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Cyclic Test Time Augmentation with Entropy Weight Method—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Contrastive Learning is Just Meta-Learning—0
Theoretical Analysis of Consistency Regularization with Limited Augmented Data—0
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization—0
Best Practices in Pool-based Active Learning for Image Classification—0
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
An object-centric sensitivity analysis of deep learning based instance segmentation—0
A General Analysis of Example-Selection for Stochastic Gradient Descent—0
Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Noisy Adversarial Training—0
AlignMix: Improving representations by interpolating aligned features—0
Learnability and Expressiveness in Self-Supervised Learning—0
Momentum as Variance-Reduced Stochastic Gradient—0
Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond—0
Prediction of the Facial Growth Direction is Challenging—0
A novel network training approach for open set image recognition—0
Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data AugmentationCode0
Layer-Parallel Training of Residual Networks with Auxiliary Variables—0
DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift—0
Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble—0
Dense Contrastive Visual-Linguistic Pretraining—0
Faithful Target Attribute Prediction in Neural Machine TranslationCode0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks—0
ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization—0
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical OverlapCode0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
Single Person Pose Estimation: A Survey—0
Digital Signal Processing Using Deep Neural Networks—0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—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