SOTAVerified

Image Augmentation

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing

( Image credit: Kornia )

Papers

Showing 221230 of 308 papers

TitleStatusHype
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References0
FitVid: High-Capacity Pixel-Level Video Prediction0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge0
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image CaptioningCode0
Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework0
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRICode0
Compound Figure Separation of Biomedical Images with Side LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified