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 241–250 of 308 papers

TitleStatusHype
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Visual Question Generation from Radiology ImagesCode1
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
Image Augmentations for GAN Training—0
Polarimetric image augmentation—0
Medical Image Generation using Generative Adversarial Networks—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge—0
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified