  Rapid development of large-scale pre-training has resulted in foundation
models that can act as effective feature extractors on a variety of downstream
tasks and domains. Motivated by this, we study the efficacy of pre-trained
vision models as a foundation for downstream continual learning (CL) scenarios.
Our goal is twofold. First, we want to understand the compute-accuracy
trade-off between CL in the raw-data space and in the latent space of
pre-trained encoders. Second, we investigate how the characteristics of the
encoder, the pre-training algorithm and data, as well as of the resulting
latent space affect CL performance. For this, we compare the efficacy of
various pre-trained models in large-scale benchmarking scenarios with a vanilla
replay setting applied in the latent and in the raw-data space. Notably, this
study shows how transfer, forgetting, task similarity and learning are
dependent on the input data characteristics and not necessarily on the CL
algorithms. First, we show that under some circumstances reasonable CL
performance can readily be achieved with a non-parametric classifier at
negligible compute. We then show how models pre-trained on broader data result
in better performance for various replay sizes. We explain this with
representational similarity and transfer properties of these representations.
Finally, we show the effectiveness of self-supervised pre-training for
downstream domains that are out-of-distribution as compared to the pre-training
domain. We point out and validate several research directions that can further
increase the efficacy of latent CL including representation ensembling. The
diverse set of datasets used in this study can serve as a compute-efficient
playground for further CL research. The codebase is available under
https://github.com/oleksost/latent_CL.
