Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting
This addresses the problem of representation degradation in continual learning for AI systems, revealing a critical bottleneck that is incremental to existing research.
The paper investigates whether catastrophic forgetting in continual learning occurs at the representation level, finding that while absolute feature forgetting is small, it is as catastrophic relative to learned knowledge as output-level forgetting and significantly hinders knowledge accumulation.
Continual learning research has shown that neural networks suffer from catastrophic forgetting "at the output level", but it is debated whether this is also the case at the level of learned representations. Multiple recent studies ascribe representations a certain level of innate robustness against forgetting -- that they only forget minimally in comparison with forgetting at the output level. We revisit and expand upon the experiments that revealed this difference in forgetting and illustrate the coexistence of two phenomena that affect the quality of continually learned representations: knowledge accumulation and feature forgetting. Taking both aspects into account, we show that, even though forgetting in the representation (i.e. feature forgetting) can be small in absolute terms, when measuring relative to how much was learned during a task, forgetting in the representation tends to be just as catastrophic as forgetting at the output level. Next we show that this feature forgetting is problematic as it substantially slows down the incremental learning of good general representations (i.e. knowledge accumulation). Finally, we study how feature forgetting and knowledge accumulation are affected by different types of continual learning methods.