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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7501–7525 of 8378 papers

TitleStatusHype
Bag of Tricks for In-Distribution Calibration of Pretrained TransformersCode0
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial NetworksCode0
Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep ChemometricsCode0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
Generating Synthetic Speech from SpokenVocab for Speech TranslationCode0
Data Augmentation of Bridging the Delay Gap for DL-based Massive MIMO CSI FeedbackCode0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
Stochastic Optimization of Plain Convolutional Neural Networks with Simple methodsCode0
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum StructureCode0
Data augmentation instead of explicit regularizationCode0
[Re] Warm-Starting Neural Network TrainingCode0
Reweighting Augmented Samples by Minimizing the Maximal Expected LossCode0
Generating Synthetic Data for Text RecognitionCode0
Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop AnnotationsCode0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Data Augmentation Generative Adversarial NetworksCode0
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AICode0
Data Augmentation for Skin Lesion AnalysisCode0
METER: a mobile vision transformer architecture for monocular depth estimationCode0
Ubicoustics: Plug-and-Play Acoustic Activity RecognitionCode0
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge BasesCode0
Generating Images of the M87* Black Hole Using GANsCode0
Data Augmentation for Robust Character Detection in Fantasy NovelsCode0
VM-NeRF: Tackling Sparsity in NeRF with View MorphingCode0
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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