  We introduce CULT (Continual Unsupervised Representation Learning with
Typicality-Based Environment Detection), a new algorithm for continual
unsupervised learning with variational auto-encoders. CULT uses a simple
typicality metric in the latent space of a VAE to detect distributional shifts
in the environment, which is used in conjunction with generative replay and an
auxiliary environmental classifier to limit catastrophic forgetting in
unsupervised representation learning. In our experiments, CULT significantly
outperforms baseline continual unsupervised learning approaches. Code for this
paper can be found here: https://github.com/oliveradk/cult
