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:

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5326–5350 of 8378 papers

TitleStatusHype
3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges—0
Document Image Layout Analysis via Explicit Edge Embedding Network—0
Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and LocalizationCode1
Towards Better Data Augmentation using Wasserstein Distance in Variational Auto-encoder—0
CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification ModelsCode0
Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation—0
Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Deep convolutional recurrent neural network for short-interval EEG motor imagery classification—0
Target-Side Data Augmentation for Sequence GenerationCode1
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
AdaAug: Learning Class- and Instance-adaptive Data Augmentation PoliciesCode1
AlignMix: Improving representations by interpolating aligned features—0
Sample-specific and Context-aware Augmentation for Long Tail Image Classification—0
Cyclic Test Time Augmentation with Entropy Weight Method—0
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning—0
An object-centric sensitivity analysis of deep learning based instance segmentation—0
A General Analysis of Example-Selection for Stochastic Gradient Descent—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Momentum as Variance-Reduced Stochastic Gradient—0
Understanding Overfitting in Reweighting Algorithms for Worst-group Performance—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