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 3301–3350 of 8378 papers

TitleStatusHype
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMsCode0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Boosting Semi-Supervised 2D Human Pose Estimation by Revisiting Data Augmentation and Consistency TrainingCode0
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance—0
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models—0
A Practical Method for Generating String CounterfactualsCode0
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition—0
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm—0
Affine transformation estimation improves visual self-supervised learning—0
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer LearningCode0
Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration—0
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization—0
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation—0
Advancing Data-driven Weather Forecasting: Time-Sliding Data Augmentation of ERA5—0
MAFIA: Multi-Adapter Fused Inclusive LanguAge Models—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
Understanding Test-Time Augmentation—0
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Evaluation Metrics for Text Data Augmentation in NLP—0
Pushing Boundaries: Mixup's Influence on Neural Collapse—0
Text Role Classification in Scientific Charts Using Multimodal TransformersCode0
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft LabelsCode0
A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation—0
Neural Models for Source Code Synthesis and Completion—0
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource RegimesCode0
PAC Learnability under Explanation-Preserving Graph Perturbations—0
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning—0
Improved Generalization of Weight Space Networks via AugmentationsCode0
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques—0
Adversarial Data Augmentation for Robust Speaker Verification—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification—0
Diabetes detection using deep learning techniques with oversampling and feature augmentation—0
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
A Data-Driven Analysis of Robust Automatic Piano Transcription—0
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of MinoritiesCode0
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models—0
Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic InterpolationCode0
Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning—0
Masked Conditional Diffusion Model for Enhancing Deepfake Detection—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic DataCode0
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations—0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
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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