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

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Showing 6501–6525 of 8378 papers

TitleStatusHype
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural NetworkCode0
Data augmentation by morphological mixup for solving Raven's Progressive Matrices—0
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN—0
Analysis of Convolutional Decoder for Image Caption Generation—0
Simplicial RegularizationCode0
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 DetectionCode0
FloMo: Tractable Motion Prediction with Normalizing FlowsCode0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision—0
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks—0
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis—0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing—0
Domain Generalization: A Survey—0
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine LearningCode0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Pseudo-labeling for Scalable 3D Object Detection—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Accounting for Variance in Machine Learning Benchmarks—0
On the Fairness of Generative Adversarial Networks (GANs)—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—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