Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation
This addresses the challenge of interpretability in neural networks for users, but it is incremental as it builds on existing visualization and interactive learning approaches.
The paper tackles the problem of neural networks being a black box by designing an interactive learning system that generates digit patterns and visualizes their recognition in real time to improve user understanding, with usability evaluated through multiple datasets and informal user testing in a workshop.
Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying computation and meaning are hidden. Due to this nature, users often face difficulties in interpreting the underlying mechanism of the NNs and the benefits of using them. In this paper, to improve users' learning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize them in real time. To help users clearly understand the visual differences of digit patterns (i.e., 0 ~ 9) and their results with an NN, integrating visualization is considered to present all digit patterns in a two-dimensional display space with supporting multiple user interactions. An evaluation with multiple datasets is conducted to determine its usability for active learning. In addition, informal user testing is managed during a summer workshop by asking the workshop participants to use the system.