One important desideratum of lifelong learning aims to discover novel classes
from unlabelled data in a continuous manner. The central challenge is twofold:
discovering and learning novel classes while mitigating the issue of
catastrophic forgetting of established knowledge. To this end, we introduce a
new paradigm called Adaptive Discovering and Merging (ADM) to discover novel
categories adaptively in the incremental stage and integrate novel knowledge
into the model without affecting the original knowledge. To discover novel
classes adaptively, we decouple representation learning and novel class
discovery, and use Triple Comparison (TC) and Probability Regularization (PR)
to constrain the probability discrepancy and diversity for adaptive category
assignment. To merge the learned novel knowledge adaptively, we propose a
hybrid structure with base and novel branches named Adaptive Model Merging
(AMM), which reduces the interference of the novel branch on the old classes to
preserve the previous knowledge, and merges the novel branch to the base model
without performance loss and parameter growth. Extensive experiments on several
datasets show that ADM significantly outperforms existing class-incremental
Novel Class Discovery (class-iNCD) approaches. Moreover, our AMM also benefits
the class-incremental Learning (class-IL) task by alleviating the catastrophic
forgetting problem.

提出了一种自适应发现和合并（ADM）的新范式，用于在增量阶段自适应地发现新类别并将新知识整合到模型中，通过减少对旧类别的干扰来保留先前知识，并在不降低性能和参数增长的情况下将新分支合并到基本模型中，从而显著优于现有的类别增量性新类别发现方法（class-iNCD）并减轻类别增量性学习（class-IL）任务中的灾难性遗忘问题。