Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization

Understand

Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human life.

• Taking the advancements from Explainable Artificial Intelligent, this paper proposes a novel technique called Auto DeepVis to dissect catastrophic forgetting in continual learning.
• A new method to deal with catastrophic forgetting named critical freezing is also introduced upon investigating the dilemma by Auto DeepVis.
• Experiments on a captioning model meticulously present how catastrophic forgetting happens, particularly showing which components are forgetting or changing.