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 5851–5900 of 8378 papers

TitleStatusHype
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction—0
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection—0
NDH-Full: Learning and Evaluating Navigational Agents on Full-Length DialogueCode0
Tencent Translation System for the WMT21 News Translation Task—0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug andPlay Data Augmentation—0
TransAug: Translate as Augmentation for Sentence Embeddings—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Residual Relaxation for Multi-view Representation Learning—0
Sayer: Using Implicit Feedback to Optimize System Policies—0
Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization—0
Predictive Geological Mapping with Convolution Neural Network Using Statistical Data Augmentation on a 3D ModelCode0
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata—0
Fast Video-based Face Recognition in Collaborative Learning Environments—0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition—0
Generating artificial texts as substitution or complement of training data—0
Gophormer: Ego-Graph Transformer for Node Classification—0
LAE : Long-tailed Age Estimation—0
Learning to Estimate Without BiasCode0
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks—0
Generative Networks for Precision Enthusiasts—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks—0
Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis—0
Semi-supervised Domain Adaptation for Semantic Segmentation—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
PatchAugment: Local Neighborhood Augmentation in Point Cloud ClassificationCode0
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning—0
Deep CNNs for Peripheral Blood Cell Classification—0
Reminding the Incremental Language Model via Data-Free Self-Distillation—0
Network Augmentation for Tiny Deep Learning—0
Towards Robust Waveform-Based Acoustic Models—0
Can Synthetic Translations Improve Bitext Quality?—0
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference—0
Curriculum Data Augmentation for Low-Resource Slides Summarization—0
Towards Identity Preserving Normal to Dysarthric Voice Conversion—0
Unifying Cross-lingual Summarization and Machine Translation with Compression RateCode0
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation—0
Reappraising Domain Generalization in Neural Networks—0
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation—0
Interpreting the Robustness of Neural NLP Models to Textual Perturbations—0
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance—0
Nuisance-Label Supervision: Robustness Improvement by Free Labels—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Semantically Distributed Robust Optimization for Vision-and-Language InferenceCode0
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models—0
Augmenting Imitation Experience via Equivariant Representations—0
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking—0
Retrieval-guided Counterfactual Generation for QA—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