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 111–120 of 308 papers

TitleStatusHype
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Policy Gradient-Driven Noise MaskCode0
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
DiffClass: Diffusion-Based Class Incremental Learning—0
Outline-Guided Object Inpainting with Diffusion Models—0
Fiducial Focus Augmentation for Facial Landmark Detection—0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified