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 131–140 of 308 papers

TitleStatusHype
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques—0
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images—0
Improving Fairness using Vision-Language Driven Image AugmentationCode0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Selective Volume Mixup for Video Action RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified