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 3851–3900 of 8378 papers

TitleStatusHype
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
A Systematic Study on Quantifying Bias in GAN-Augmented Data—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Targeted Data Augmentation for bias mitigation—0
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial NetworksCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious CorrelationsCode0
Robust Fraud Detection via Supervised Contrastive Learning—0
Distributionally Robust Cross Subject EEG Decoding—0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets—0
Data augmentation and explainability for bias discovery and mitigation in deep learning—0
Generative Machine Listener—0
A tailored Handwritten-Text-Recognition System for Medieval Latin—0
CCFace: Classification Consistency for Low-Resolution Face Recognition—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image SegmentationCode0
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions—0
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays—0
Graph Out-of-Distribution Generalization with Controllable Data Augmentation—0
Robust Autonomous Vehicle Pursuit without Expert Steering Labels—0
Quantifying Overfitting: Introducing the Overfitting Index—0
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluationCode0
Deep Learning based Tomato Disease Detection and Remedy Suggestions using Mobile Application—0
Accurate synthesis of Dysarthric Speech for ASR data augmentation—0
Automated ensemble method for pediatric brain tumor segmentation—0
Semantic Equivariant Mixup—0
DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut LearningCode0
ADRMX: Additive Disentanglement of Domain Features with Remix Loss—0
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Feature Matching Data Synthesis for Non-IID Federated Learning—0
Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillationCode0
I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection—0
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging—0
Key Gene Mining in Transcriptional Regulation for Specific Biological Processes with Small Sample Sizes Using Multi-network pipeline Transformer—0
MedMine: Examining Pre-trained Language Models on Medication MiningCode0
PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning—0
Predicting Group Choices from Group Profiles—0
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory—0
WeldMon: A Cost-effective Ultrasonic Welding Machine Condition Monitoring System—0
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
From Fake to Hyperpartisan News Detection Using Domain Adaptation—0
Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational ForecastingCode0
Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces—0
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection—0
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation—0
Graph Contrastive Learning with Generative Adversarial Network—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—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