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 3551–3600 of 8378 papers

TitleStatusHype
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
A Systematic Study on Quantifying Bias in GAN-Augmented Data—0
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning—0
Adaptive Regularization of Labels—0
A Causal View on Robustness of Neural Networks—0
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting—0
Generalizability through Explainability: Countering Overfitting with Counterfactual Examples—0
Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation—0
Controllable Top-down Feature Transformer—0
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks—0
Controllable Text Simplification with Explicit Paraphrasing—0
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space—0
Generalization bounds via distillation—0
GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU—0
Generalization Gap in Data Augmentation: Insights from Illumination—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
DeepC2: AI-powered Covert Command and Control on OSNs—0
GCC: Generative Color Constancy via Diffusing a Color Checker—0
Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Generalization to translation shifts: a study in architectures and augmentations—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
Generalized Data Augmentation for Low-Resource Translation—0
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory—0
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Generalizing Back-Translation in Neural Machine Translation—0
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation—0
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning—0
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback—0
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models—0
GASE: Generatively Augmented Sentence Encoding—0
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
GANs 'N Lungs: improving pneumonia prediction—0
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation—0
Generating Data Augmentation samples for Semantic Segmentation of Salt Bodies in a Synthetic Seismic Image Dataset—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
Generating Diverse Translation by Manipulating Multi-Head Attention—0
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction—0
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—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