With the memory-resource-limited constraints, class-incremental learning
(CIL) usually suffers from the "catastrophic forgetting" problem when updating
the joint classification model on the arrival of newly added classes. To cope
with the forgetting problem, many CIL methods transfer the knowledge of old
classes by preserving some exemplar samples into the size-constrained memory
buffer. To utilize the memory buffer more efficiently, we propose to keep more
auxiliary low-fidelity exemplar samples rather than the original real
high-fidelity exemplar samples. Such a memory-efficient exemplar preserving
scheme makes the old-class knowledge transfer more effective. However, the
low-fidelity exemplar samples are often distributed in a different domain away
from that of the original exemplar samples, that is, a domain shift. To
alleviate this problem, we propose a duplet learning scheme that seeks to
construct domain-compatible feature extractors and classifiers, which greatly
narrows down the above domain gap. As a result, these low-fidelity auxiliary
exemplar samples have the ability to moderately replace the original exemplar
samples with a lower memory cost. In addition, we present a robust classifier
adaptation scheme, which further refines the biased classifier (learned with
the samples containing distillation label knowledge about old classes) with the
help of the samples of pure true class labels. Experimental results demonstrate
the effectiveness of this work against the state-of-the-art approaches.

本研究针对类增量学习时可能出现的 “灾难性遗忘” 问题，提出了一种记忆资源高效利用的样本保存方案，并结合一种主副样本协作学习方案，以缓解样本来源、质量不同带来的问题。实验结果表明该方案在类增量学习中具有较好的性能表现。