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 121–130 of 308 papers

TitleStatusHype
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining—0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified