Title: Design and Evaluation for Robust Continual Learning. Abstract: Continual learning is the ability to learn from new experiences without forgetting
previous experiences. Different continual learning methods are each motivated
by their own interpretation of the continual learning scenario, resulting in a wide
variety of experiment protocols, which hinders understanding and comparison of
results. Existing works emphasize differences in accuracy without considering
the effects of experimental settings. However, understanding the effects of experimental
assumptions is the most crucial part of any evaluation, as the experimental
protocol may supply implicit information. We propose six rules as a guideline for
experimental design and execution to conduct robust continual learning evaluation
for better understanding of the methods. Using these rules, we demonstrate the
importance of experimental choices regarding the sequence of incoming data and
the sequence of the task oracle. Even when task oracle-based methods are desired,
the rules can guide experimental design to support better evaluation and understanding
of the continual learning methods. Consistent application of these rules
in evaluating continual learning methods makes explicit the effect and validity of
many assumptions, thereby avoiding misleading conclusions.