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 3251–3300 of 8378 papers

TitleStatusHype
ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation—0
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—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
Fractal interpolation in the context of prediction accuracy optimization—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification—0
Improving Socratic Question Generation using Data Augmentation and Preference OptimizationCode0
VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction—0
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds—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
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric EstimationCode0
Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells—0
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction—0
Why does music source separation benefit from cacophony?—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time AugmentationCode0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Data augmentation method for modeling health records with applications to clopidogrel treatment failure detection—0
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data—0
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—0
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification—0
A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classificationCode0
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding—0
Prompt Perturbation Consistency Learning for Robust Language Models—0
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical StudyCode0
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models—0
ChildAugment: Data Augmentation Methods for Zero-Resource Children's Speaker VerificationCode0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening—0
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition—0
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario—0
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks—0
FormulaReasoning: A Dataset for Formula-Based Numerical ReasoningCode0
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation—0
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems—0
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network—0
Revisiting Data Augmentation in Deep Reinforcement LearningCode0
HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?Code0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMsCode0
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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