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 3651–3700 of 8378 papers

TitleStatusHype
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Generative Data Augmentation for Vehicle Detection in Aerial Images—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification—0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
Boosting Masked Face Recognition with Multi-Task ArcFace—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning—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
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation—0
Digital Signal Processing Using Deep Neural Networks—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—0
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation—0
Hydranet: Data Augmentation for Regression Neural Networks—0
HyperTime: Implicit Neural Representation for Time Series—0
GenLabel: Mixup Relabeling using Generative Models—0
Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Image compositing is all you need for data augmentation—0
Improved Image-based Pose Regressor Models for Underwater Environments—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
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Exploring Geometric Consistency for Monocular 3D Object Detection—0
Digital Operating Mode Classification of Real-World Amateur Radio Transmissions—0
Auctus: A Dataset Search Engine for Data Augmentation—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle—0
German Phoneme Recognition with Text-to-Phoneme Data Augmentation—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—0
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements—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
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions—0
Human Pose Transfer with Augmented Disentangled Feature Consistency—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