Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning

by   Yujiang He, et al.

Continual learning can enable neural networks to evolve by learning new tasks sequentially in task-changing scenarios. However, two general and related 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.


page 1

page 2

page 3

page 4

page 5

page 6


Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts

Compared with traditional deep learning techniques, continual learning e...

Overcoming the Stability Gap in Continual Learning

In many real-world applications, deep neural networks are retrained from...

A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Despite the growing interest in continual learning, most of its contempo...

SIESTA: Efficient Online Continual Learning with Sleep

In supervised continual learning, a deep neural network (DNN) is updated...

Continual Learning Augmented Investment Decisions

Investment decisions can benefit from incorporating an accumulated knowl...

Continual Learning Approaches for Anomaly Detection

Anomaly Detection is a relevant problem that arises in numerous real-wor...

BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning

Continual Learning (CL) is the process of learning ceaselessly a sequenc...

Please sign up or login with your details

Forgot password? Click here to reset