Recommendation of data-free class-incremental learning algorithms by simulating future data

arXiv:2403.18132v1 Announce Type: new
Abstract: Class-incremental learning deals with sequential data streams composed of batches of classes. Various algorithms have been proposed to address the challenging case where samples from past classes cannot be stored. However, selecting an appropriate algorithm for a user-defined setting is an open problem, as the relative performance of these algorithms depends on the incremental settings. To solve this problem, we introduce an algorithm recommendation method that simulates the future data stream. Given an initial set …

abstract algorithm algorithms arxiv case class cs.ai cs.cv cs.lg data data streams deals free future however incremental recommendation samples type