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 1901–1950 of 8378 papers

TitleStatusHype
AnatoMix: Anatomy-aware Data Augmentation for Multi-organ Segmentation—0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges—0
Emergent Equivariance in Deep Ensembles—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal DecouplingCode1
A Generative Model of Symmetry TransformationsCode0
AS-ES Learning: Towards Efficient CoT Learning in Small Models—0
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image ClassificationCode1
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language ModelsCode1
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation—0
Fine Tuning vs. Retrieval Augmented Generation for Less Popular KnowledgeCode0
Multi-level Product Category Prediction through Text ClassificationCode0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation—0
OpenGraph: Towards Open Graph Foundation ModelsCode3
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
Improving Socratic Question Generation using Data Augmentation and Preference OptimizationCode0
Fractal interpolation in the context of prediction accuracy optimization—0
VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—0
Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification—0
Learning to Find Missing Video Frames with Synthetic Data Augmentation: A General Framework and Application in Generating Thermal Images Using RGB Cameras—0
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance—0
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds—0
WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model ResearchCode1
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric EstimationCode0
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction—0
Data augmentation method for modeling health records with applications to clopidogrel treatment failure detection—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells—0
Why does music source separation benefit from cacophony?—0
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labellingCode1
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time AugmentationCode0
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation—0
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—0
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset—0
CodeS: Towards Building Open-source Language Models for Text-to-SQLCode2
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classificationCode0
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical StudyCode0
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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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified