ContinualAI Reading Group: A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

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Keiland Cooper

Mar 17, 2021, 7:12:06 AM3/17/21
to Continual Learning & AI News
Hi All,

This Friday 19-03-2021, 5.30pm CET, for the ContinualAI Reading Group, Thang Doan (McGill University) will present the paper:

Abstract: Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although major advances have been made in the field, one recurring problem which remains unsolved is that of Catastrophic Forgetting (CF). While the issue has been extensively studied empirically, little attention has been paid from a theoretical angle. In this paper, we show that the impact of CF increases as two tasks increasingly align. We introduce a measure of task similarity called the NTK overlap matrix which is at the core of CF. We analyze common projected gradient algorithms and demonstrate how they mitigate forgetting. Then, we propose a variant of Orthogonal Gradient Descent (OGD) which leverages structure of the data through Principal Component Analysis (PCA). Experiments support our theoretical findings and show how our method can help reduce CF on classical CL datasets.

The event will be moderated by: Vincenzo Lomonaco.

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Please also contact me if you want to speak at one of the next sessions!

All the best,
Keiland Cooper

University of California
ContinualAI Board Member
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