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 201–210 of 308 papers

TitleStatusHype
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual PoliciesCode1
Survey: Image Mixing and Deleting for Data AugmentationCode0
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture—0
Salient Objects in ClutterCode1
Few-Shot Learning for Image Classification of Common FloraCode0
InAugment: Improving Classifiers via Internal AugmentationCode1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified