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 191200 of 308 papers

TitleStatusHype
Randomize to Generalize: Domain Randomization for Runway FOD Detection0
RaViTT: Random Vision Transformer Tokens0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology0
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies0
rQdia: Regularizing Q-Value Distributions With Image Augmentation0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0Unverified