On the importance of cross-task features for class-incremental learning

A. Soutif-Cormerais, Marc Masana, J. van de Weijer, B. Twardowski

Publikation: Andere BeiträgeSonstiger Beitrag


In class-incremental learning, an agent with limited resources needs to learn a sequence of classification tasks, forming an ever growing classification problem, with the constraint of not being able to access data from previous tasks. The main difference with task-incremental learning, where a task-ID is available at inference time, is that the learner also needs to perform cross-task discrimination, i.e. distinguish between classes that have not been seen together. Approaches to tackle this problem are numerous and mostly make use of an external memory (buffer) of non-negligible size. In this paper, we ablate the learning of cross-task features and study its influence on the performance of basic replay strategies used for class-IL. We also define a new forgetting measure for class-incremental learning, and see that forgetting is not the principal cause of low performance. Our experimental results show that future algorithms for class-incremental learning should not only prevent forgetting, but also aim to improve the quality of the cross-task features. This is especially important when the number of classes per task is small.
PublikationsstatusVeröffentlicht - 22 Juni 2021


Untersuchen Sie die Forschungsthemen von „On the importance of cross-task features for class-incremental learning“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren