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 2801–2850 of 8378 papers

TitleStatusHype
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data—0
Synthetic Data Aided Federated Learning Using Foundation Models—0
Enhanced Long-Tailed Recognition with Contrastive CutMix AugmentationCode0
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
A Survey of Data Synthesis ApproachesCode0
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality AssessmentCode0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Query-oriented Data Augmentation for Session Search—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation—0
Self-supervised Vision Transformer are Scalable Generative Models for Domain GeneralizationCode0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?Code0
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets—0
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)—0
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing—0
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions—0
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label SmoothingCode0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
LLM-Generated Natural Language Meets Scaling Laws: New Explorations and Data Augmentation Methods—0
Exact Bayesian Gaussian Cox Processes Using Random IntegralCode0
A Survey on Deep Clustering: From the Prior Perspective—0
Mining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentation—0
RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Effects of Using Synthetic Data on Deep Recommender Models' Performance—0
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
Sequential Disentanglement by Extracting Static Information From A Single Sequence Element—0
View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis—0
Improving Grammatical Error Correction via Contextual Data AugmentationCode0
Generative Expansion of Small Datasets: An Expansive Graph Approach—0
Detection of Synthetic Face Images: Accuracy, Robustness, Generalization—0
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation—0
Sound Tagging in Infant-centric Home Soundscapes—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Improving robustness to corruptions with multiplicative weight perturbationsCode0
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency—0
Meta-learning and Data Augmentation for Stress Testing Forecasting ModelsCode0
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language UnderstandingCode0
Task Oriented In-Domain Data Augmentation—0
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—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