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 61–70 of 308 papers

TitleStatusHype
Implicit Semantic Data Augmentation for Deep NetworksCode1
Masked Autoencoders are Robust Data AugmentorsCode1
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified