Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner Freddie Bickford Smith Qixuan Feng Yee Whye Teh Yarin Gal
Sequential Bayesian inference over predictive functions is a natural
framework for continual learning from streams of data. However, applying it to
neural networks has proved challenging in practice. Addressing the drawbacks of
existing techniques, we propose an optimization objective derived by
formulating continual learning as sequential function-space variational
inference. In contrast to existing methods that regularize neural network
parameters directly, this objective allows parameters to vary widely during
training, enabling better adaptation to new tasks. Compared to objectives that
directly regularize neural network predictions, the proposed objective allows
for more flexible variational distributions and more effective regularization.
We demonstrate that, across a range of task sequences, neural networks trained
via sequential function-space variational inference achieve better predictive
accuracy than networks trained with related methods while depending less on
maintaining a set of representative points from previous tasks.