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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 308 papers

TitleStatusHype
A Survey on Data Augmentation in Large Model EraCode2
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
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified