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
Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification—0
FairGen: Towards Fair Graph Generation—0
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
Bora: Biomedical Generalist Video Generation Model—0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
FairSkin: Fair Diffusion for Skin Disease Image Generation—0
Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis—0
Autoencoder Image Interpolation by Shaping the Latent Space—0
Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning—0
Generalizability through Explainability: Countering Overfitting with Counterfactual Examples—0
Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for Improved Text-Based Learning for LGE Detection—0
Fake it till you predict it: data augmentation strategies to detect initiation and termination of oncology treatment—0
FakeLocator: Robust Localization of GAN-Based Face Manipulations—0
Comparing Methods for Bias Mitigation in Graph Neural Networks—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
Fall Detection for Smart Living using YOLOv5—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy—0
Complementary Systems for Off-Topic Spoken Response Detection—0
Farm land weed detection with region-based deep convolutional neural networks—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—0
Distillation of Diffusion Features for Semantic Correspondence—0
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls—0
Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation—0
Generalization in Instruction Following Systems—0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data—0
Adversarial Backdoor Defense in CLIP—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Compositional Attribute Imbalance in Vision Datasets—0
Fast Cross-domain Data Augmentation through Neural Sentence Editing—0
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers—0
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering—0
Compositional Generalization for Kinship Prediction through Data Augmentation—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
General GAN-generated image detection by data augmentation in fingerprint domain—0
Fast Hand Detection in Collaborative Learning Environments—0
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering—0
Assessing the Feasibility of Internet-Sourced Video for Automatic Cattle Lameness Detection—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Fast Video-based Face Recognition in Collaborative Learning Environments—0
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network—0
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—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