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 71–80 of 308 papers

TitleStatusHype
BGM: Background Mixup for X-ray Prohibited Items Detection—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Diffusion Models for Robotic Manipulation: A Survey—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions—0
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified