A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross MacIejewski

Research output: Contribution to journalArticlepeer-review

Abstract

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.

Original languageEnglish (US)
Article number9219240
Pages (from-to)1385-1395
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

Keywords

  • Transfer learning
  • deep learning
  • visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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