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 161170 of 308 papers

TitleStatusHype
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Towards Composable Distributions of Latent Space Augmentations0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
LMSeg: Language-guided Multi-dataset Segmentation0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification0
A convolutional neural network of low complexity for tumor anomaly detection0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay0
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks0
Design of Arabic Sign Language Recognition Model0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified