[Paper] Temporal Taskification in Streaming Continual Learning: A Source of Evaluation Instability

Summary

Streaming Continual Learning (CL) typically converts continuous data into discrete tasks through temporal partitioning. This paper argues that this "temporal taskification" is not merely a preprocessing step but a structural component of evaluation, influencing CL regimes and benchmark conclusions. Different valid splits of the same data stream can induce varying results, highlighting a significant source of evaluation instability in the field.

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