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 271–280 of 308 papers

TitleStatusHype
Isometric Transformations for Image Augmentation in Mueller Matrix PolarimetryCode0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer SensorsCode0
CIA: Controllable Image Augmentation Framework Based on Stable DiffusionCode0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Random Transformation of Image Brightness for Adversarial AttackCode0
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and EstimationCode0
DenseNet Models for Tiny ImageNet ClassificationCode0
Learning deep illumination-robust features from multispectral filter array imagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified