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Interpreting the behaviors of Deep Neural Networks (usually considered as ablack box) is critical especially when they are now being widely adopted overdiverse aspects of human life. Taking the advancements from ExplainableArtificial Intelligent, this paper proposes a novel technique called AutoDeepVis to dissect catastrophic forgetting in continual learning. A new methodto deal with catastrophic forgetting named critical freezing is also introducedupon investigating the dilemma by Auto DeepVis. Experiments on a captioningmodel meticulously present how catastrophic forgetting happens, particularlyshowing which components are forgetting or changing. The effectiveness of ourtechnique is then assessed; and more precisely, critical freezing claims thebest performance on both previous and coming tasks over baselines, proving thecapability of the investigation. Our techniques could not only be supplementaryto existing solutions for completely eradicating catastrophic forgetting forlife-long learning but also explainable.

Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization