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

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Showing 7276–7300 of 8378 papers

TitleStatusHype
Fast Parallel SVM using Data Augmentation—0
Fast Video-based Face Recognition in Collaborative Learning Environments—0
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network—0
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Feature-based Style Randomization for Domain Generalization—0
Feature-level augmentation to improve robustness of deep neural networks to affine transformations—0
Feature-level Malware Obfuscation in Deep Learning—0
Feature Matching Data Synthesis for Non-IID Federated Learning—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Feature Space Transfer for Data Augmentation—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
Feature Weaken: Vicinal Data Augmentation for Classification—0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks—0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features—0
Ferrograph image classification—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