  This study investigates an application of a new probabilistic interpretation
of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism
wildly used in classification and object recognition.
  However, a softmax mechanism forces a model to operate under a closed-set
paradigm, i.e., to predict an object class out of a set of pre-defined labels.
  This characteristic contributes to efficacy in classification, but poses a
risk of non-sense prediction in object recognition.
  Object recognition is often operated under a dynamic and diverse condition.
  A foreign object -- an object of any unprepared class -- can be encountered
at any time.
  OSR is intended to address an issue of identifying a foreign object in object
recognition.
  Based on Bayes theorem and the emphasis of conditioning on the context,
softmax inference has been re-interpreted.
  This re-interpretation has led to a new approach to OSR, called Latent
Cognizance (LC). Our investigation employs various scenarios, using Imagenet
2012 dataset as well as fooling and open-set images. The findings support LC
hypothesis and show its effectiveness on OSR.
