Closed youtao1 closed 6 months ago
Thanks for your interest in this work! I can see the confusion, let me try to clarify. The problem that is described in ref [17], learning to drive in different weather conditions, is a domain-incremental learning problem because, in general, solving this problem does not require identifying the weather condition. However, as for any domain-incremental learning problem, one possible solution to this problem is through first identifying the domain (in this case the weather condition), which then allows the use of task- or domain-specific components. This is the kind of solution that is taken by DISC. So the problem that is described in [17] does not require inference of task-ID, but the solution they propose does. Hope this helps!
In your paper "Three types of incremental learning" domain increments do not require contextual identity to be inferred. But in the paper you cited "[17] An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions" it is mentioned that "A requirement of DISC and the proposed domain-IL scenario is to have access to the task-ID during inference , as it is done in task-incremental learning approaches”. Is it because the task_ID can be obtained through the car sensor, so there is no need to infer the contextual identity?