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 1101–1150 of 8378 papers

TitleStatusHype
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models—0
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation ChallengeCode0
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Contrastive Learning for Character Detection in Ancient Greek PapyriCode0
Deep-Wide Learning Assistance for Insect Pest ClassificationCode1
Robust image representations with counterfactual contrastive learningCode1
Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing TechniquesCode0
Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
Effective Pre-Training of Audio Transformers for Sound Event DetectionCode1
From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT ImagingCode1
NBBOX: Noisy Bounding Box Improves Remote Sensing Object DetectionCode0
An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Test-time Training for Hyperspectral Image Super-resolution—0
GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map ConstructionCode1
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection—0
AutoPET Challenge: Tumour Synthesis for Data Augmentation—0
Multi-scale decomposition of sea surface height snapshots using machine learningCode0
Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
Synthetic continued pretrainingCode2
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking—0
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image ClassificationCode1
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget—0
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets—0
Enhanced Generative Data Augmentation for Semantic Segmentation via Stronger GuidanceCode0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation—0
Efficient Classification of Histopathology Images—0
EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling CorrectionCode0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
A Survey on Diffusion Models for Recommender SystemsCode2
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models—0
FreeAugment: Data Augmentation Search Across All Degrees of FreedomCode0
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease ClassificationCode0
Phrase-Level Adversarial Training for Mitigating Bias in Neural Network-based Automatic Essay Scoring—0
Medical Image Segmentation via Single-Source Domain Generalization with Random Amplitude Spectrum SynthesisCode0
Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone—0
Bi-modality Images Transfer with a Discrete Process Matching Method—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