Web: http://arxiv.org/abs/2201.02478

Jan. 10, 2022, 2:10 a.m. | Ching-Chun Chang

cs.LG updates on arXiv.org arxiv.org

Recent advances in deep learning have led to a paradigm shift in reversible
steganography. A fundamental pillar of reversible steganography is predictive
modelling which can be realised via deep neural networks. However, non-trivial
errors exist in inferences about some out-of-distribution and noisy data. In
view of this issue, we propose to consider uncertainty in predictive models
based upon a theoretical framework of Bayesian deep learning. Bayesian neural
networks can be regarded as self-aware machinery; that is, a machine that knows
its own limitations. To quantify uncertainty, we approximate the posterior …

arxiv bayesian for networks neural neural networks

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