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 2551–2600 of 8378 papers

TitleStatusHype
Improving satellite imagery segmentation using multiple Sentinel-2 revisitsCode0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
Vision-based Xylem Wetness Classification in Stem Water Potential Determination—0
Machine learning approaches for automatic defect detection in photovoltaic systemsCode0
Adversarial Backdoor Defense in CLIP—0
Strategies for Improving NL-to-FOL Translation with LLMs: Data Generation, Incremental Fine-Tuning, and VerificationCode0
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AICode0
Region Mixup—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
Enabling Tensor Decomposition for Time-Series Classification via A Simple Pseudo-Laplacian Contrast—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
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers—0
WeatherFormer: Empowering Global Numerical Weather Forecasting with Space-Time Transformer—0
Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks—0
PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion CaptureCode0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
RRM: Robust Reward Model Training Mitigates Reward Hacking—0
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation—0
A Multi-LLM Debiasing Framework—0
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning—0
Test-Time Augmentation Meets Variational Bayes—0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT DistillationCode0
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model—0
AutoPET III Challenge: PET/CT Semantic Segmentation—0
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning—0
SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline InferenceCode0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
EventAug: Multifaceted Spatio-Temporal Data Augmentation Methods for Event-based Learning—0
NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration—0
Bridging Domain Gap for Flight-Ready Spaceborne Vision—0
Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis—0
Synthetic data augmentation for robotic mobility aids to support blind and low vision people—0
ShapeAug++: More Realistic Shape Augmentation for Event Data—0
Contrastive Learning for Character Detection in Ancient Greek PapyriCode0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models—0
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation ChallengeCode0
Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing TechniquesCode0
Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild—0
NBBOX: Noisy Bounding Box Improves Remote Sensing Object DetectionCode0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation—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