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 7251–7300 of 8378 papers

TitleStatusHype
Lightspeed Geometric Dataset Distance via Sliced Optimal TransportCode0
Speaker-Follower Models for Vision-and-Language NavigationCode0
Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown ExplorationCode0
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)Code0
Lightweight Defense Against Adversarial Attacks in Time Series ClassificationCode0
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient TuningCode0
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss FunctionCode0
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?Code0
Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language ConditioningCode0
High-dimensional Bayesian Tobit regression for censored response with Horseshoe priorCode0
Beyond One-Hot Labels: Semantic Mixing for Model CalibrationCode0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Hierarchical Transformer Model for Scientific Named Entity RecognitionCode0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Urban Sound Tagging using Convolutional Neural NetworksCode0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast CompetitionCode0
Line Detection and Segmentation of Annual Crops Using Hybrid MethodCode0
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image ModelsCode0
AIC CTU system at AVeriTeC: Re-framing automated fact-checking as a simple RAG taskCode0
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question MatchingCode0
Link Representation Learning for Probabilistic Travel Time EstimationCode0
A Guide for Practical Use of ADMG Causal Data AugmentationCode0
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluationCode0
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound ClassificationCode0
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and BeyondCode0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizabilityCode0
ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT ClassificationCode0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizationCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound ClassificationCode0
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity ReasoningCode0
Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholdingCode0
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading ComprehensionCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Deep ChArUco: Dark ChArUco Marker Pose EstimationCode0
Regularizing Deep Neural Networks with Stochastic Estimators of Hessian TraceCode0
DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationCode0
LLM-powered Data Augmentation for Enhanced Cross-lingual PerformanceCode0
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference ResolutionCode0
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive StudyCode0
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained SettingsCode0
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNetCode0
Reinforced Counterfactual Data Augmentation for Dual Sentiment ClassificationCode0
Reinforcement Learning for Portfolio ManagementCode0
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node AtlasCode0
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image SegmentationCode0
AraSpot: Arabic Spoken Command SpottingCode0
Deep Bayesian Active Semi-Supervised LearningCode0
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous ControlCode0
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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