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 7501–7550 of 8378 papers

TitleStatusHype
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Generative Machine Listener—0
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images—0
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market—0
Generative Models for Multi-Illumination Color Constancy—0
Generative Networks for Precision Enthusiasts—0
Generative Retrieval for Book search—0
Generative Robust Classification—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
GenLabel: Mixup Relabeling using Generative Models—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
GenScan: A Generative Method for Populating Parametric 3D Scan Datasets—0
GenX: Mastering Code and Test Generation with Execution Feedback—0
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates—0
Geometric Data Augmentation Based on Feature Map Ensemble—0
Exploring Geometric Consistency for Monocular 3D Object Detection—0
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving—0
German Phoneme Recognition with Text-to-Phoneme Data Augmentation—0
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals—0
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation—0
Gibbs Max-margin Topic Models with Data Augmentation—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Global and Local Information Based Deep Network for Skin Lesion Segmentation—0
Global Context Is All You Need for Parallel Efficient Tractography Parcellation—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Global Mixup: Eliminating Ambiguity with Clustering Relationships—0
Global Mixup: Eliminating Ambiguity with Clustering—0
Global Wheat Challenge 2020: Analysis of the competition design and winning models—0
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Good Data Is All Imitation Learning Needs—0
Good-Enough Example Extrapolation—0
Gophormer: Ego-Graph Transformer for Node Classification—0
Go Small and Similar: A Simple Output Decay Brings Better Performance—0
Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction—0
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization—0
Gradient-based Data Augmentation for Semi-Supervised Learning—0
Gradient Mask: Lateral Inhibition Mechanism Improves Performance in Artificial Neural Networks—0
GradMix for nuclei segmentation and classification in imbalanced pathology image datasets—0
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Graph Augmentation for Cross Graph Domain Generalization—0
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Contrastive Learning with Personalized Augmentation—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