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 121130 of 308 papers

TitleStatusHype
Practical X-ray Gastric Cancer Diagnostic Support Using Refined Stochastic Data Augmentation and Hard Boundary Box TrainingCode0
Survey: Image Mixing and Deleting for Data AugmentationCode0
A machine-generated catalogue of Charon's craters and implications for the Kuiper beltCode0
CIA: Controllable Image Augmentation Framework Based on Stable DiffusionCode0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Albumentations: fast and flexible image augmentationsCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
Learning to Compose Domain-Specific Transformations for Data AugmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified