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 7501–7550 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
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identificationCode0
Data Augmentation for Machine Translation via Dependency Subtree SwappingCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Stochastic Pooling for Regularization of Deep Convolutional Neural NetworksCode0
Generated Graph DetectionCode0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
MicAugment: One-shot Microphone Style TransferCode0
Bad Global Minima Exist and SGD Can Reach ThemCode0
Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape ModelCode0
Data Augmentation for Low-Resource Named Entity Recognition Using BacktranslationCode0
Data Augmentation for Low-Resource Keyphrase GenerationCode0
Using mixup as regularization and tuning hyper-parameters for ResNetsCode0
Straight to Shapes++: Real-time Instance Segmentation Made More AccurateCode0
General-to-Detailed GAN for Infrequent Class Medical ImagesCode0
Generalizing to Unseen Domains via Adversarial Data AugmentationCode0
Robust 2D/3D Vehicle Parsing in Arbitrary Camera Views for CVISCode0
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
UFM: Unified Feature Matching Pre-training with Multi-Modal Image AssistantsCode0
What's in a Question: Using Visual Questions as a Form of SupervisionCode0
Minor changes make a difference: a case study on the consistency of UD-based dependency parsersCode0
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related FeaturesCode0
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
Diverse Gaussian Noise Consistency Regularization for Robustness and Uncertainty CalibrationCode0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
MISLEADER: Defending against Model Extraction with Ensembles of Distilled ModelsCode0
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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