  Continual learning (CL) aims to incrementally learn different tasks (such as
classification) in a non-stationary data stream without forgetting old ones.
Most CL works focus on tackling catastrophic forgetting under a
learning-from-scratch paradigm. However, with the increasing prominence of
foundation models, pre-trained models equipped with informative representations
have become available for various downstream requirements. Several CL methods
based on pre-trained models have been explored, either utilizing pre-extracted
features directly (which makes bridging distribution gaps challenging) or
incorporating adaptors (which may be subject to forgetting). In this paper, we
propose a concise and effective approach for CL with pre-trained models. Given
that forgetting occurs during parameter updating, we contemplate an alternative
approach that exploits training-free random projectors and class-prototype
accumulation, which thus bypasses the issue. Specifically, we inject a frozen
Random Projection layer with nonlinear activation between the pre-trained
model's feature representations and output head, which captures interactions
between features with expanded dimensionality, providing enhanced linear
separability for class-prototype-based CL. We also demonstrate the importance
of decorrelating the class-prototypes to reduce the distribution disparity when
using pre-trained representations. These techniques prove to be effective and
circumvent the problem of forgetting for both class- and domain-incremental
continual learning. Compared to previous methods applied to pre-trained
ViT-B/16 models, we reduce final error rates by between 20% and 62% on seven
class-incremental benchmarks, despite not using any rehearsal memory. We
conclude that the full potential of pre-trained models for simple, effective,
and fast CL has not hitherto been fully tapped. Code is at
github.com/RanPAC/RanPAC.
