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 2601–2650 of 8378 papers

TitleStatusHype
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Test-time Training for Hyperspectral Image Super-resolution—0
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection—0
AutoPET Challenge: Tumour Synthesis for Data Augmentation—0
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
Multi-scale decomposition of sea surface height snapshots using machine learningCode0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
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
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
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
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation—0
Enhanced Generative Data Augmentation for Semantic Segmentation via Stronger GuidanceCode0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
EdaCSC: Two Easy Data Augmentation Methods for Chinese Spelling CorrectionCode0
Efficient Classification of Histopathology Images—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models—0
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
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease ClassificationCode0
FreeAugment: Data Augmentation Search Across All Degrees of FreedomCode0
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection—0
Bi-modality Images Transfer with a Discrete Process Matching Method—0
Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone—0
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis—0
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised LearningCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data SynthesisCode0
Adversarial Learning for Neural PDE Solvers with Sparse Data—0
A Comparative Study of Pre-training and Self-trainingCode0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Semantically Controllable Augmentations for Generalizable Robot Learning—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
IVGF: The Fusion-Guided Infrared and Visible General Framework—0
A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches—0
Defending against Model Inversion Attacks via Random Erasing—0
GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU—0
Data Augmentation for Image Classification using Generative AI—0
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine—0
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models—0
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?—0
Flexible framework for generating synthetic electrocardiograms and photoplethysmogramsCode0
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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