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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 8378 papers

TitleStatusHype
Good Data Is All Imitation Learning Needs—0
Improving satellite imagery segmentation using multiple Sentinel-2 revisitsCode0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
Small data deep learning methodology for in-field disease detection—0
WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial NetworksCode0
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition—0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and VerificationCode0
Machine learning approaches for automatic defect detection in photovoltaic systemsCode0
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based ModelsCode1
Adversarial Backdoor Defense in CLIP—0
Vision-based Xylem Wetness Classification in Stem Water Potential Determination—0
Self-Supervised Any-Point Tracking by Contrastive Random WalksCode2
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AICode0
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks—0
Region Mixup—0
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading ComprehensionCode0
Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks—0
WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer—0
PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion CaptureCode0
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation—0
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
Data Augmentation for Sequential Recommendation: A SurveyCode3
RRM: Robust Reward Model Training Mitigates Reward Hacking—0
A Multi-LLM Debiasing Framework—0
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT DistillationCode0
AutoPET III Challenge: PET/CT Semantic Segmentation—0
HSIGene: A Foundation Model For Hyperspectral Image GenerationCode2
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline InferenceCode0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning—0
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model—0
Test-Time Augmentation Meets Variational Bayes—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning—0
NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration—0
Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology—0
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis—0
ShapeAug++: More Realistic Shape Augmentation for Event Data—0
Synthetic data augmentation for robotic mobility aids to support blind and low vision people—0
oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models—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