  Incrementally training deep neural networks to recognize new classes is a
challenging problem. Most existing class-incremental learning methods store
data or use generative replay, both of which have drawbacks, while
'rehearsal-free' alternatives such as parameter regularization or
bias-correction methods do not consistently achieve high performance. Here, we
put forward a new strategy for class-incremental learning: generative
classification. Rather than directly learning the conditional distribution
p(y|x), our proposal is to learn the joint distribution p(x,y), factorized as
p(x|y)p(y), and to perform classification using Bayes' rule. As a
proof-of-principle, here we implement this strategy by training a variational
autoencoder for each class to be learned and by using importance sampling to
estimate the likelihoods p(x|y). This simple approach performs very well on a
diverse set of continual learning benchmarks, outperforming generative replay
and other existing baselines that do not store data.
