Physical experiments often involve multiple imaging representations, such as
X-ray scans and microscopic images. Deep learning models have been widely used
for supervised analysis in these experiments. Combining different image
representations is frequently required to analyze and make a decision properly.
Consequently, multi-view data has emerged - datasets where each sample is
described by views from different angles, sources, or modalities. These
problems are addressed with the concept of multi-view learning. Understanding
the decision-making process of deep learning models is essential for reliable
and credible analysis. Hence, many explainability methods have been devised
recently. Nonetheless, there is a lack of proper explainability in multi-view
models, which are challenging to explain due to their architectures. In this
paper, we suggest different multi-view architectures for the vision domain,
each suited to another problem, and we also present a methodology for
explaining these models. To demonstrate the effectiveness of our methodology,
we focus on the domain of High Energy Density Physics (HEDP) experiments, where
multiple imaging representations are used to assess the quality of foam
samples. We apply our methodology to classify the foam samples quality using
the suggested multi-view architectures. Through experimental results, we
showcase the improvement of accurate architecture choice on both accuracy - 78%
to 84% and AUC - 83% to 93% and present a trade-off between performance and
explainability. Specifically, we demonstrate that our approach enables the
explanation of individual one-view models, providing insights into the
decision-making process of each view. This understanding enhances the
interpretability of the overall multi-view model. The sources of this work are
available at:
this https URL

本文提出了不同的多视角架构用于视觉领域，并介绍了一种解释这些模型的方法。通过实验结果，展示了准确架构选择对于泡沫样本质量分类的改善，并呈现了性能和可解释性之间的权衡。具体而言，我们证明了我们的方法能够解释单个单视角模型，从而增强了整体多视图模型的可解释性。