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Towards Transparent Systems: Semantic Characterization of Failure Modes

  • Aayush Bansal
  • Ali Farhadi
  • Devi Parikh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8694)

Abstract

Today’s computer vision systems are not perfect. They fail frequently. Even worse, they fail abruptly and seemingly inexplicably. We argue that making our systems more transparent via an explicit human understandable characterization of their failure modes is desirable. We propose characterizing the failure modes of a vision system using semantic attributes. For example, a face recognition system may say “If the test image is blurry, or the face is not frontal, or the person to be recognized is a young white woman with heavy make up, I am likely to fail.” This information can be used at training time by researchers to design better features, models or collect more focused training data. It can also be used by a downstream machine or human user at test time to know when to ignore the output of the system, in turn making it more reliable. To generate such a “specification sheet”, we discriminatively cluster incorrectly classified images in the semantic attribute space using L1-regularized weighted logistic regression. We show that our specification sheets can predict oncoming failures for face and animal species recognition better than several strong baselines. We also show that lay people can easily follow our specification sheets.

Keywords

Vision System Failure Mode Discriminative Function Semantic Attribute Failure Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aayush Bansal
    • 1
  • Ali Farhadi
    • 2
  • Devi Parikh
    • 3
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.University of WashingtonSeattleUSA
  3. 3.Virginia TechBlacksburgUSA

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