Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
This is an incremental tool for domain scientists working with X-ray scattering image analysis to interpret their deep learning models.
The researchers developed a visualization system to help domain scientists understand deep learning models that extract multiple structural attributes from X-ray scattering images, enabling interactive exploration of feature spaces and classification outputs with preliminary case studies demonstrating its functionality.
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images. The system focuses on studying the model behaviors related to multiple structural attributes. It allows users to explore the images in the feature space, the classification output of different attributes, with respect to the actual attributes labelled by domain scientists. Abundant interactions allow users to flexibly select instance images, their clusters, and compare them visually in details. Two preliminary case studies demonstrate its functionalities and usefulness.