ContinualAI Seminars: “Reducing Representation Drift in Online Continual Learning“
Continual Learning Seminar: “Reducing Representation Drift in Online Continual Learning“
Abstract:
In the online continual learning paradigm, agents must learn from a changing
distribution while respecting memory and compute constraints. Previous work
in this setting often tries to reduce catastrophic forgetting by limiting changes in
the space of model parameters. In this work we instead 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. Starting from a popular approach, experience replay, we consider
metric learning based loss functions which, when adjusted to appropriately select
negative samples, can effectively nudge the learned representations to be more
robust to new future classes. We show that this selection of negatives is in fact
critical for reducing representation drift of previously observed data. Moti
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