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

Showing 251260 of 308 papers

TitleStatusHype
FusiformNet: Extracting Discriminative Facial Features on Different Levels0
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
CIMON: Towards High-quality Hash Codes0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch0
Face Mask Detection using Transfer Learning of InceptionV30
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning0
A Technical Report for VIPriors Image Classification Challenge0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning0
Image Augmentations for GAN Training0
Polarimetric image augmentation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified