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:

( Image credit: Albumentations )

Papers

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Showing 3601–3625 of 8378 papers

TitleStatusHype
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
GANs 'N Lungs: improving pneumonia prediction—0
Generating Intermediate Steps for NLI with Next-Step Supervision—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
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—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
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Generating Synthetic Multispectral Satellite Imagery from Sentinel-2—0
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy—0
GAN based Data Augmentation to Resolve Class Imbalance—0
GAN-based Data Augmentation for Chest X-ray Classification—0
Contrastive Visual Data Augmentation—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Generation of Synthetic Electronic Medical Record Text—0
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
GAMMA: Generative Augmentation for Attentive Marine Debris Detection—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