Abstract
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.
Users
Please
log in to take part in the discussion (add own reviews or comments).