Unsupervised continual learning by cross-level, instance-group and pseudo-group discrimination with hard attention

Extensive work has been done in supervised continual learning (SCL) , wherein models adapt to changing distributions with labeled data while mitigating catastrophic forgetting. However, this approach diverges from real-world scenarios where labeled data is scarce or non-existent. Unsupervised continual learning (UCL) emerges to bridge this disparity. Previous research has explored methods for unsupervised continuous feature learning by incorporating rehearsal to alleviate the problem of catastrophic forgetting. Although these techniques are effective, they may not be feasible for scenarios where storing training data is impractical. Moreover, rehearsal techniques may confront challenges pertaining to representation drifts and overfitting, particularly under limited buffer size conditions. To address these drawbacks, we employ parameter isolation as a strategy to mitigate forgetting. Specifically, we use task-specific hard attention to prevent updates to parameters important for previous tasks. In contrastive learning, loss is prone to be negatively affected by a reduction in the diversity of negative samples. Therefore, we incorporate instance-to-instance similarity into contrastive learning through both direct instance grouping and discrimination at the cross-level with local instance groups, as well as with local pseudo-instance groups. The masked model learns the features using cross-level discrimination, which naturally clusters similar data in the representation space. Extensive experimentation demonstrates that our proposed approach outperforms current state-of-the-art (SOTA) baselines by significant margins, all while exhibiting minimal or nearly zero forgetting, and without the need for any rehearsal buffer. Additionally, the model learns distinct task boundaries. It achieves an overall-average task and class incremental learning (TIL & CIL) accuracy of 76.79% and 62.96% respectively with nearly zero forgetting, across standard datasets for varying task sequences ranging from 5 to 100. This surpasses SOTA baselines, which only reach 74.28% and 60.68% respectively in the UCL setting, where they experience substantial forgetting of almost over 4%. Moreover, our approach achieves performance nearly comparable to the SCL baseline and even surpasses it on some standard datasets, with a notable reduction in forgetting from almost 14.51% to nearly zero.