Reviews: Uncertainty-based Continual Learning with Adaptive Regularization

Summary: The paper presents a regularization-based continual learning method, UCL, where during the training of the current task the parameters of the network are regularized based on their uncertainty in the previous tasks (less uncertainty means that a parameter is important and should not be altered in future tasks). Instead of measuring the uncertainty at the parameter level, as done in the earlier works (e.g.) Variational Continual Learning (VCL), the authors propose to measure uncertainty over the neurons resulting in less number of learnable parameters (mean and variances) to store. To compute the neurons uncertainty, UCL imposes a constraint that all the weights going into a neuron share the same/ common variance. To learn the parameters, a variational objective is used where the authors cleverly opened up the KL term in the ELBO and play with it to impose constraints on the variances of different neurons. The results are reported on MNIST benchmarks and RL tasks.