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 1351–1400 of 8378 papers

TitleStatusHype
Invariance Principle Meets Vicinal Risk Minimization—0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 LanguagesCode2
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-InstructCode1
Fine-Grained and Interpretable Neural Speech EditingCode1
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image GenerationCode1
On the power of data augmentation for head pose estimationCode1
SmurfCat at PAN 2024 TextDetox: Alignment of Multilingual Transformers for Text DetoxificationCode0
Enhanced Long-Tailed Recognition with Contrastive CutMix AugmentationCode0
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data—0
Synthetic Data Aided Federated Learning Using Foundation Models—0
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation—0
LLMAEL: Large Language Models are Good Context Augmenters for Entity LinkingCode1
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality AssessmentCode0
Query-oriented Data Augmentation for Session Search—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect DetectionCode1
A Survey of Data Synthesis ApproachesCode0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—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
HRSAM: Efficient Interactive Segmentation in High-Resolution ImagesCode1
Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse WeatherCode2
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets—0
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?Code0
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly DetectionCode0
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing—0
Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution AnalysisCode2
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)—0
SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism—0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label SmoothingCode0
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions—0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
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
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
Exact Bayesian Gaussian Cox Processes Using Random IntegralCode0
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction—0
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Sequential Disentanglement by Extracting Static Information From A Single Sequence Element—0
Effects of Using Synthetic Data on Deep Recommender Models' Performance—0
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
RouteLLM: Learning to Route LLMs with Preference DataCode7
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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