Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning
Y. He, Z. Huang, and B. Sick. Workshop on Interactive Machine Learning Workshop (IMLW), AAAI, (2022)(accepted).
Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios.
However, two general challenges should be overcome in further research before we apply this technique to real-world applications.
Firstly, newly collected novelties from the data stream in applications could contain anomalies that are meaningless for continual learning.
Instead of viewing them as a new task for updating, we have to filter out such anomalies to reduce the disturbance of extremely high-entropy data for the progression of convergence.
Secondly, fewer efforts have been put into research regarding the explainability of continual learning, which leads to a lack of transparency and credibility of the updated neural networks.
Elaborated explanations about the process and result of continual learning can help experts in judgment and making decisions.
Therefore, we propose the conceptual design of an explainability module with experts in the loop based on techniques, such as dimension reduction, visualization, and evaluation strategies.
This work aims to overcome the mentioned challenges by sufficiently explaining and visualizing the identified anomalies and the updated neural network.
With the help of this module, experts can be more confident in decision-making regarding anomaly filtering, dynamic adjustment of hyperparameters, data backup, etc.