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 91–100 of 308 papers

TitleStatusHype
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts—0
Spatially Visual Perception for End-to-End Robotic Learning—0
CIA: Controllable Image Augmentation Framework Based on Stable DiffusionCode0
Isometric Transformations for Image Augmentation in Mueller Matrix PolarimetryCode0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images—0
Few-shot target-driven instance detection based on open-vocabulary object detection models—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified