Spurious Features in Continual Learning

Timothée Lesort

Keywords: Spurious Features, Continual Learning

TL;DR: study the problem of spurious features for continual learning.

Abstract:

Continual Learning (CL) is the research field addressing training settings where the data distribution is not static. One of the core problems CL addresses is learning without forgetting.
To solve problems, continual learning algorithms need to learn robust and stable representations based only on a subset of the data. Those representations are necessarily biased and should be revisited when new data becomes available.
This paper studies spurious features' influence on continual learning algorithms.
We show that in continual learning, algorithms have to deal with local spurious features that correlate well with labels within a task only but which are not good representations for the concept to learn.
One of the big challenges of continual learning algorithms is to discover causal relationships between features and labels under distribution shifts.