Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery

TL;DR

This paper introduces CAMP, a novel method combining a learnable projector and feature distillation to improve continual category discovery by balancing adaptation to new categories and retention of old knowledge.

Contribution

It proposes a new approach integrating a learnable projector with feature distillation and an auxiliary network, enhancing model adaptability in GCCD tasks.

Findings

CAMP outperforms existing methods in GCCD scenarios.

The combination of components yields significant performance improvements.

CAMP effectively balances learning new categories and retaining old knowledge.

Abstract

Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model's ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector with feature distillation, thus enhancing model adaptability without sacrificing past knowledge. The resulting distribution shift of the previously learned categories is mitigated with the auxiliary category adaptation network. We demonstrate that while each component offers modest benefits individually, their combination - dubbed CAMP (Category Adaptation Meets Projected distillation) - significantly improves the balance between learning new information and retaining old. CAMP exhibits superior performance across several GCCD and Class Incremental Learning scenarios. The code is available at https://github.com/grypesc/CAMP.