  Continual learning aims to learn new tasks incrementally using less
computation and memory resources instead of retraining the model from scratch
whenever new task arrives. However, existing approaches are designed in
supervised fashion assuming all data from new tasks have been manually
annotated, which are not practical for many real-life applications. In this
work, we propose to use pseudo label instead of the ground truth to make
continual learning feasible in unsupervised mode. The pseudo labels of new data
are obtained by applying global clustering algorithm and we propose to use the
model updated from last incremental step as the feature extractor. Due to the
scarcity of existing work, we introduce a new benchmark experimental protocol
for unsupervised continual learning of image classification task under
class-incremental setting where no class label is provided for each incremental
learning step. Our method is evaluated on the CIFAR-100 and ImageNet (ILSVRC)
datasets by incorporating the pseudo label with various existing supervised
approaches and show promising results in unsupervised scenario.
