CUCL: Codebook for Unsupervised Continual Learning
Chen Cheng Jingkuan Song Xiaosu Zhu Junchen Zhu Lianli Gao Hengtao Shen
The focus of this study is on Unsupervised Continual Learning (UCL), as it
presents an alternative to Supervised Continual Learning which needs
high-quality manual labeled data. The experiments under the UCL paradigm
indicate a phenomenon where the results on the first few tasks are suboptimal.
This phenomenon can render the model inappropriate for practical applications.
To address this issue, after analyzing the phenomenon and identifying the lack
of diversity as a vital factor, we propose a method named Codebook for
Unsupervised Continual Learning (CUCL) which promotes the model to learn
discriminative features to complete the class boundary. Specifically, we first
introduce a Product Quantization to inject diversity into the representation
and apply a cross quantized contrastive loss between the original
representation and the quantized one to capture discriminative information.
Then, based on the quantizer, we propose an effective Codebook Rehearsal to
address catastrophic forgetting. This study involves conducting extensive
experiments on CIFAR100, TinyImageNet, and MiniImageNet benchmark datasets. Our
method significantly boosts the performances of supervised and unsupervised
methods. For instance, on TinyImageNet, our method led to a relative
improvement of 12.76% and 7% when compared with Simsiam and BYOL, respectively.