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SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks
Moreover, the uncertainty map of the proposed SUPER-Net associates low confidence (or equivalently high uncertainty) to patches in the test input images that are corrupted with noise, artifacts, or adversarial attacks.
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs).
Self-Compression in Bayesian Neural Networks
We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the network.
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations.
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