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

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Showing 281–290 of 308 papers

TitleStatusHype
Siamese Networks for Cat Re-Identification: Exploring Neural Models for Cat Instance RecognitionCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving AugmentationCode0
Augmentor: An Image Augmentation Library for Machine LearningCode0
Learning to Compose Domain-Specific Transformations for Data AugmentationCode0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
Data Augmentation via Levy ProcessesCode0
A machine-generated catalogue of Charon's craters and implications for the Kuiper beltCode0
Sparse Signal Models for Data Augmentation in Deep Learning ATRCode0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified