Class-incremental Learning with Pre-allocated Fixed Classifiers

TL;DR

This paper introduces a fixed classifier approach for class-incremental learning, replacing expanding classifiers with pre-allocated nodes to improve feature stability and learning efficiency.

Contribution

It proposes a novel fixed classifier method that maintains consistent output nodes from the start, enhancing feature stability and learning in incremental class scenarios.

Findings

Achieves comparable performance to expanding classifiers.

Exhibits stable feature representations during incremental learning.

Validated through extensive experiments and ablation studies.

Abstract

In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes. In this work, we substitute the expanding classifier with a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model. Experiments with public datasets show that the proposed approach is as effective as the expanding classifier while exhibiting novel intriguing properties of the internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.