Deep learning methods are increasingly becoming instrumental as modeling
tools in computational neuroscience, employing optimality principles to build
bridges between neural responses and perception or behavior. Developing models
that adequately represent uncertainty is however challenging for deep learning
methods, which often suffer from calibration problems. This constitutes a
difficulty in particular when modeling cortical circuits in terms of Bayesian
inference, beyond single point estimates such as the posterior mean or the
maximum a posteriori. In this work we systematically studied uncertainty
representations in latent representations of variational auto-encoders (VAEs),
both in a perceptual task from natural images and in two other canonical tasks
of computer vision, finding a poor alignment between uncertainty and
informativeness or ambiguities in the images. We next showed how a novel
approach which we call explaining-away variational auto-encoders (EA-VAEs),
fixes these issues, producing meaningful reports of uncertainty in a variety of
scenarios, including interpolation, image corruption, and even
out-of-distribution detection. We show EA-VAEs may prove useful both as models
of perception in computational neuroscience and as inference tools in computer
vision.

深度学习方法在计算神经科学中作为建模工具变得越来越重要，本文系统地研究了变分自编码器的不确定性表示，提出了一种新的方法解释偏离变分自编码器 (EA-VAEs)，并发现它在各种情况下能够产生有意义的不确定性报告，可在计算神经科学的感知模型和计算机视觉的推理工具中发挥作用。