Many applications of computer vision require the ability to adapt to novel
data distributions after deployment. Adaptation requires algorithms capable of
continual learning (CL). Continual learners must be plastic to adapt to novel
tasks while minimizing forgetting of previous tasks.However, CL opens up
avenues for noise to enter the training pipeline and disrupt the CL. This work
focuses on label noise and instance noise in the context of class-incremental
learning (CIL), where new classes are added to a classifier over time, et
there is no access to external data from past classes. We aim to understand the
sensitivity of CL methods that work by replaying items from a memory
constructed using the idea of Coresets. We derive a new bound for the
robustness of such a method to uncorrelated instance noise under a general
additive noise threat model, revealing several insights. Putting the theory
into practice, we create two continual learning algorithms to construct
noise-tolerant replay buffers. We empirically compare the effectiveness of
prior memory-based continual learners and the proposed algorithms under label
and uncorrelated instance noise on five diverse datasets. We show that existing
memory-based CL are not robust whereas the proposed methods exhibit significant
improvements in maximizing classification accuracy and minimizing forgetting in
the noisy CIL setting.

Cet article explore les excursions dans le temps et leurs implications.

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