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.

Further readings:

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Papers

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Showing 1751–1800 of 8378 papers

TitleStatusHype
FairSHAP: Preprocessing for Fairness Through Attribution-Based Data AugmentationCode0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Reconstructing Syllable Sequences in Abugida Scripts with Incomplete Inputs—0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges—0
Completely Weakly Supervised Class-Incremental Learning for Semantic Segmentation—0
Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming—0
Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI SegmentationCode0
SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition—0
A Generative Neural Annealer for Black-Box Combinatorial Optimization—0
Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach—0
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning—0
High-dimensional Bayesian Tobit regression for censored response with Horseshoe priorCode0
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix—0
Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion—0
Detecting Prefix Bias in LLM-based Reward Models—0
A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Images—0
Addressing degeneracies in latent interpolation for diffusion models—0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
TiSpell: A Semi-Masked Methodology for Tibetan Spelling Correction covering Multi-Level Error with Data AugmentationCode0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
Deep Learning for On-Street Parking Violation Prediction—0
Transformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization NetworkCode0
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images—0
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing—0
Guidance for Intra-cardiac Echocardiography Manipulation to Maintain Continuous Therapy Device Tip Visibility—0
White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection—0
Model-Based Closed-Loop Control Algorithm for Stochastic Partial Differential Equation Control—0
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models—0
Semantic Style Transfer for Enhancing Animal Facial Landmark Detection—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model—0
Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence—0
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation—0
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets—0
Point Cloud Recombination: Systematic Real Data Augmentation Using Robotic Targets for LiDAR Perception Validation—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Bemba Speech Translation: Exploring a Low-Resource African Language—0
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity RecognitionCode0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
Lightweight Defense Against Adversarial Attacks in Time Series ClassificationCode0
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data AugmentationCode0
Data-Driven Optical To Thermal Inference in Pool Boiling Using 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