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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 8378 papers

TitleStatusHype
Narrowing Class-Wise Robustness Gaps in Adversarial Training—0
TULIP: Towards Unified Language-Image Pretraining—0
Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion ModelsCode0
Binary AddiVortes: (Bayesian) Additive Voronoi Tessellations for Binary Classification with an application to Predicting Home Mortgage Application Outcomes—0
Second language Korean Universal Dependency treebank v1.2: Focus on data augmentation and annotation scheme refinementCode0
Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey—0
PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Data—0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing—0
PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modelling and machine learning—0
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language ModelsCode1
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation—0
Fast data augmentation for battery degradation prediction—0
BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature AnalysisCode1
Accessibility Considerations in the Development of an AI Action Plan—0
A Survey on SAR ship classification using Deep Learning—0
Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation—0
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generationCode0
BEVDiffLoc: End-to-End LiDAR Global Localization in BEV View based on Diffusion ModelCode1
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
Data augmentation using diffusion models to enhance inverse Ising inference—0
Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation—0
Context-guided Responsible Data Augmentation with Diffusion ModelsCode0
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey—0
ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias MitigationCode0
Robust Asymmetric Heterogeneous Federated Learning with Corrupted ClientsCode0
DAST: Difficulty-Aware Self-Training on Large Language Models—0
External Knowledge Injection for CLIP-Based Class-Incremental LearningCode2
Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosisCode1
Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models—0
A Grey-box Text Attack Framework using Explainable AI—0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
Automating Violence Detection and Categorization from Ancient Texts—0
Global Context Is All You Need for Parallel Efficient Tractography Parcellation—0
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks—0
SHIP: A Shapelet-based Approach for Interpretable Patient-Ventilator Asynchrony Detection—0
Why Pre-trained Models Fail: Feature Entanglement in Multi-modal Depression Detection—0
Handwritten Digit Recognition: An Ensemble-Based Approach for Superior Performance—0
An Empirical Study of Causal Relation Extraction Transfer: Design and Data—0
Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases—0
End-to-End Action Segmentation Transformer—0
Learning to Drive by Imitating Surrounding Vehicles—0
Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature PerturbationCode0
Disconnect to Connect: A Data Augmentation Method for Improving Topology Accuracy in Image SegmentationCode0
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning—0
SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging—0
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models—0
Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals—0
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks—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