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The Transform-and-Perform framework: Explainable deep learning beyond classification
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  • Vidya Prasad ,
  • Ruud van Sloun ,
  • Stef van den Elzen ,
  • Anna Vilanova ,
  • Nicola Pezzotti
Vidya Prasad
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Ruud van Sloun
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Stef van den Elzen
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Anna Vilanova
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Nicola Pezzotti
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Abstract

In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides guidelines to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We also illustrate how T&P effectively supports the understanding and identifying potential gaps in existing VA systems.
2022Published in IEEE Transactions on Visualization and Computer Graphics on pages 1-14. 10.1109/TVCG.2022.3219248