Explaining How Deep Neural Networks Forget by Deep Visualization
This addresses the problem of improving explainability and performance in continual learning for AI systems, though it appears incremental as it builds on existing interpretable ML approaches.
The paper tackled catastrophic forgetting in continual learning by proposing a novel tool, Catastrophic Forgetting Dissector (CFD), to visualize and explain how deep neural networks forget, and introduced a new method called Critical Freezing that significantly outperforms recent techniques.
Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this paper proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual learning settings. We also introduce a new method called Critical Freezing based on the observations of our tool. Experiments on ResNet articulate how catastrophic forgetting happens, particularly showing which components of this famous network are forgetting. Our new continual learning algorithm defeats various recent techniques by a significant margin, proving the capability of the investigation. Critical freezing not only attacks catastrophic forgetting but also exposes explainability.