Scalable Uncertainty for Computer Vision With Functional Variational Inference

CVPR 2020 Eduardo D. C. Carvalho Ronald Clark Andrea Nicastro Paul H. J. Kelly

As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family... (read more)

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