Bayesian Gradient Descent: Online Variational Bayes Learning with Increased Robustness to Catastrophic Forgetting and Weight Pruning
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We suggest a novel approach for the estimation of the posterior distribution of the weights of a neural network, using an online version of the variational Bayes method. Having a confidence measure of the weights allows to combat several shortcomings of neural networks, such as their parameter redundancy, and their notorious vulnerability to the change of input distribution ("catastrophic forgetting"). Specifically, We show that this approach helps alleviate the catastrophic forgetting phenomenon - even without the knowledge of when the tasks are been switched. Furthermore, it improves the robustness of the network to weight pruning - even without re-training.
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