Kavli Affiliate: Yi Zhou
| First 5 Authors: Wei Chen, Yi Zhou, , ,
| Summary:
In the realm of class-incremental learning (CIL), alleviating the
catastrophic forgetting problem is a pivotal challenge. This paper discovers a
counter-intuitive observation: by incorporating domain shift into CIL tasks,
the forgetting rate is significantly reduced. Our comprehensive studies
demonstrate that incorporating domain shift leads to a clearer separation in
the feature distribution across tasks and helps reduce parameter interference
during the learning process. Inspired by this observation, we propose a simple
yet effective method named DisCo to deal with CIL tasks. DisCo introduces a
lightweight prototype pool that utilizes contrastive learning to promote
distinct feature distributions for the current task relative to previous ones,
effectively mitigating interference across tasks. DisCo can be easily
integrated into existing state-of-the-art class-incremental learning methods.
Experimental results show that incorporating our method into various CIL
methods achieves substantial performance improvements, validating the benefits
of our approach in enhancing class-incremental learning by separating feature
representation and reducing interference. These findings illustrate that DisCo
can serve as a robust fashion for future research in class-incremental
learning.
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