Socialized Division and Collaboration: Rethinking Class-Incremental Learning under Optimization Conflicts

2026-08-21

AI Summary

本文针对类增量学习中因优化冲突导致的灾难性遗忘问题，提出一种基于社会团结理论的Socialized Division and Collaboration方法，通过专门模型分解和协作来解决。

Abstract

Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sessions. While effective under mild distributional shifts, this formulation becomes strained when successive sessions induce incompatible optimization directions, leading to destructive interference and catastrophic forgetting. We argue that such forgetting reflects a structural limitation of enforcing heterogeneous learning dynamics within a single parameter space. Motivated by social solidarity theory, we propose Socialized Division and Collaboration (SDC) as a reformulation of continual learning that decomposes session learning across specialized models in response to optimization conflicts, while enabling coordinated collaboration. To support this formulation with a principled allocation mechanism, we introduce an energy-based session-model compatibility criterion grounded in Helmholtz free energy, which guides adaptive session allocation and model evolution under conflicting objectives. This framework integrates session assignment, model evolution, and collaborative inference into a unified pipeline, offering an alternative to monolithic continual learning formulations and highlighting a broader design principle for learning under persistent optimization conflicts.

Problem

• class-incremental learning
• optimization conflicts
• catastrophic forgetting

Innovation

• Socialized Division and Collaboration
• Helmholtz free energy
• optimization conflicts
• continual learning