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Found 6 papers, 0 papers with code
Understanding Data Augmentation from a Robustness Perspective
In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness.
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation
Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches.
Style Curriculum Learning for Robust Medical Image Segmentation
A style transfer model with style fusion is employed to generate the curriculum samples.
Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
Style-invariant Cardiac Image Segmentation with Test-time Augmentation
Based on the zero-shot style transfer to remove appearance shift and test-time augmentation to explore diverse underlying anatomy, our proposed method is effective in combating the appearance shift.
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs.
