  In the online continual learning paradigm, agents must learn from a changing
distribution while respecting memory and compute constraints. Experience Replay
(ER), where a small subset of past data is stored and replayed alongside new
data, has emerged as a simple and effective learning strategy. In this work, we
focus on the change in representations of observed data that arises when
previously unobserved classes appear in the incoming data stream, and new
classes must be distinguished from previous ones. We shed new light on this
question by showing that applying ER causes the newly added classes'
representations to overlap significantly with the previous classes, leading to
highly disruptive parameter updates. Based on this empirical analysis, we
propose a new method which mitigates this issue by shielding the learned
representations from drastic adaptation to accommodate new classes. We show
that using an asymmetric update rule pushes new classes to adapt to the older
ones (rather than the reverse), which is more effective especially at task
boundaries, where much of the forgetting typically occurs. Empirical results
show significant gains over strong baselines on standard continual learning
benchmarks.
