  Unsupervised continual learning aims to learn new tasks incrementally without
requiring human annotations. However, most existing methods, especially those
targeted on image classification, only work in a simplified scenario by
assuming all new data belong to new tasks, which is not realistic if the class
labels are not provided. Therefore, to perform unsupervised continual learning
in real life applications, an out-of-distribution detector is required at
beginning to identify whether each new data corresponds to a new task or
already learned tasks, which still remains under-explored yet. In this work, we
formulate the problem for Out-of-distribution Detection in Unsupervised
Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In
addition, we propose a novel OOD detection method by correcting the output bias
at first and then enhancing the output confidence for in-distribution data
based on task discriminativeness, which can be applied directly without
modifying the learning procedures and objectives of continual learning. Our
method is evaluated on CIFAR-100 dataset by following the proposed evaluation
protocol and we show improved performance compared with existing OOD detection
methods under the unsupervised continual learning scenario.
