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We use deconvolutional generative model for feedback: satisfies Bayes rule with feedforward #NeuralNetwork Allows us to implement Bayesian brain theory. Iterative MAP inference results in recurrent feedback, enforces self-consistency. Top-down feedback modulates ReLu/other unitspic.twitter.com/KQX31Xf6vO
More iterations are needed for “harder images” i.e. stronger attacks. Our model is robust while being agnostic to attacks. Adversarial training further enhances robustness. Generative feedback also restores corrupted images: get both clean images and labels. #AI #DeepLearningpic.twitter.com/lp3NaR5H7F
