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 751–800 of 8378 papers

TitleStatusHype
EvTTC: An Event Camera Dataset for Time-to-Collision Estimation—0
Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images—0
DEIM: DETR with Improved Matching for Fast ConvergenceCode5
BhashaVerse : Translation Ecosystem for Indian Subcontinent Languages—0
Enhancing Mathematical Reasoning in LLMs with Background Operators—0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Distillation of Diffusion Features for Semantic Correspondence—0
Tight PAC-Bayesian Risk Certificates for Contrastive LearningCode0
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning—0
Curriculum-style Data Augmentation for LLM-based Metaphor Detection—0
GUESS: Generative Uncertainty Ensemble for Self Supervision—0
Many-MobileNet: Multi-Model Augmentation for Robust Retinal Disease ClassificationCode2
Evaluating the Impact of Data Augmentation on Predictive Model Performance—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation—0
Robust soybean seed yield estimation using high-throughput ground robot videos—0
ProbPose: A Probabilistic Approach to 2D Human Pose EstimationCode2
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
QA-TOOLBOX: Conversational Question-Answering for process task guidance in manufacturing—0
Su-RoBERTa: A Semi-supervised Approach to Predicting Suicide Risk through Social Media using Base Language Models—0
ECG-SleepNet: Deep Learning-Based Comprehensive Sleep Stage Classification Using ECG Signals—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation—0
Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution—0
Improving speaker verification robustness with synthetic emotional utterances—0
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Improving the performance of weak supervision searches using data augmentation—0
T2Vid: Translating Long Text into Multi-Image is the Catalyst for Video-LLMsCode1
Topology-Preserving Scaling in Data Augmentation—0
Towards Santali Linguistic Inclusion: Building the First Santali-to-English Translation Model using mT5 Transformer and Data Augmentation—0
Reverse Thinking Makes LLMs Stronger Reasoners—0
CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures—0
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image SegmentationCode1
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation—0
Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Training and Evaluating Language Models with Template-based Data GenerationCode1
Thai Financial Domain Adaptation of THaLLE -- Technical Report—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Task Progressive Curriculum Learning for Robust Visual Question Answering—0
Scaling nnU-Net for CBCT Segmentation—0
Semantic Data Augmentation for Long-tailed Facial Expression Recognition—0
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations—0
SynDiff-AD: Improving Semantic Segmentation and End-to-End Autonomous Driving with Synthetic Data from Latent Diffusion Models—0
J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation—0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Unsupervised Event Outlier Detection in Continuous Time—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