A study of the Dream Net model robustness across continual learning scenarios.

Continual learning is one of the major challenges of deep learning. For decades, many studies have proposed efficient models overcoming catastrophic forgetting when learning new data. However, as they were focused on providing the best reduce-forgetting performance, studies have moved away from real-life applications where algorithms need to adapt to changing environments and perform, no matter the type of data arrival. Therefore, there is a growing need to define new scenarios to assess the robustness of existing methods with those challenges in mind. The issue of data availability during training is another essential point in the development of solid continual learning algorithms. Depending on the streaming formulation, the model needs in the more extreme scenarios to be able to adapt to new data as soon as it arrives and without the possibility to review it afterwards. In this study, we propose a review of existing continual learning scenarios and their associated terms. Those existing terms and definitions are synthesized in an atlas in order to provide a better overview. Based on two of the main categories defined in the atlas, "Class-IL" and "Domain-IL", we define eight different scenarios with data streams of varying complexity that allow to test the models robustness in changing data arrival scenarios. We choose to evaluate Dream Net - Data Free, a privacy-preserving continual learning algorithm, in each proposed scenario and demonstrate that this model is robust enough to succeed in every proposed scenario, regardless of how the data is presented. We also show that it is competitive with other continual learning literature algorithms that are not privacy preserving which is a clear advantage for real-life human-centered applications.