Unsupervised Continual Learning Via Pseudo Labels
This addresses the impracticality of supervised continual learning for real-life applications where data lacks annotations, though it is incremental as it adapts existing supervised methods.
The paper tackles the problem of continual learning without manual annotations by using pseudo labels from global clustering and the previous model as a feature extractor, achieving promising results on CIFAR-100 and ImageNet datasets.
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.