Schedule-Robust Online Continual Learning

A hallmark of natural intelligence is its ability to continually absorb new knowledge while retaining and updating existing one. Achieving this objective in machines is the goal of continual learning (CL). Ideally, CL algorithms learn online from a never-ending and non-stationary stream of data, without catastrophic forgetting (McCloskey and Cohen, 1989; Ratcliff, 1990; French, 1999). The non-stationarity of the data stream is modeled by some schedule that defines what data arrives and how its distribution evolves over time. Two family of schedules commonly investigated are task-based (De Lange et al., 2021) and task-free (Aljundi et al., 2019a). The task-based setting assumes that new data arrives one task at a time and data distribution is stationary for each task. Many CL algorithms (e.g., Buzzega et al., 2020; Kirkpatrick et al., 2017; Hou et al., 2019) thus train offline, with multiple passes and shuffles over task data. The task-free setting does not assume the existence of separate tasks but instead expects CL algorithms to learn online from streaming data, with evolving sample distribution (Caccia et al., 2022; Shanahan et al., 2021).