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 161–170 of 308 papers

TitleStatusHype
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images—0
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection—0
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Towards Composable Distributions of Latent Space Augmentations—0
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified