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 4901–4925 of 8378 papers

TitleStatusHype
Seismic Fault SAM: Adapting SAM with Lightweight Modules and 2.5D Strategy for Fault Detection—0
SeismoFlow -- Data augmentation for the class imbalance problem—0
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation—0
Selecting task with optimal transport self-supervised learning for few-shot classification—0
Selective Data Augmentation for Robust Speech Translation—0
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information—0
Selective Synthetic Augmentation with Quality Assurance—0
Self-Adapting Language Models—0
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning—0
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI—0
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging—0
Self-Competitive Neural Networks—0
SelfD: Self-Learning Large-Scale Driving Policies From the Web—0
Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation—0
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks—0
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search—0
Self-paced Data Augmentation for Training Neural Networks—0
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks—0
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
Self-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images—0
Self-Supervised Class Incremental Learning—0
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery—0
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case—0
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations—0
Self-supervised Document Clustering Based on BERT with Data Augment—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