Robustness-preserving Lifelong Learning via Dataset Condensation
Lifelong learning (LL) aims to improve a predictive model as the data source
evolves continuously. Most work in this learning paradigm has focused on
resolving the problem of 'catastrophic forgetting,' which refers to a notorious
dilemma between improving model accuracy over new data and retaining accuracy
over previous data. Yet, it is also known that machine learning (ML) models can
be vulnerable in the sense that tiny, adversarial input perturbations can
deceive the models into producing erroneous predictions. This motivates the
research objective of this paper - specification of a new LL framework that can
salvage model robustness (against adversarial attacks) from catastrophic
forgetting. Specifically, we propose a new memory-replay LL strategy that
leverages modern bi-level optimization techniques to determine the 'coreset' of
the current data (i.e., a small amount of data to be memorized) for ease of
preserving adversarial robustness over time. We term the resulting LL framework
'Data-Efficient Robustness-Preserving LL' (DERPLL). The effectiveness of DERPLL
is evaluated for class-incremental image classification using ResNet-18 over
the CIFAR-10 dataset. Experimental results show that DERPLL outperforms the
conventional coreset-guided LL baseline and achieves a substantial improvement
in both standard accuracy and robust accuracy.