Latent Cognizance: What Machine Really Learns

TL;DR

This paper explores Latent Cognizance, a new probabilistic interpretation of recognition mechanisms, revealing hidden inference structures that could enhance open-set recognition in machine learning.

Contribution

It introduces a novel probabilistic interpretation of recognition inference based on Bayesian theorem, supported by analysis and validation in sign language recognition.

Findings

Supports the rationale behind Latent Cognizance

Reveals a hidden mechanism in classification inference

Suggests potential for scalable open-set recognition

Abstract

Despite overwhelming achievements in recognition accuracy, extending an open-set capability -- ability to identify when the question is out of scope -- remains greatly challenging in a scalable machine learning inference. A recent research has discovered Latent Cognizance (LC) -- an insight on a recognition mechanism based on a new probabilistic interpretation, Bayesian theorem, and an analysis of an internal structure of a commonly-used recognition inference structure. The new interpretation emphasizes a latent assumption of an overlooked probabilistic condition on a learned inference model. Viability of LC has been shown on a task of sign language recognition, but its potential and implication can reach far beyond a specific domain and can move object recognition toward a scalable open-set recognition. However, LC new probabilistic interpretation has not been directly investigated. This article investigates the new interpretation under a traceable context. Our findings support the rationale on which LC is based and reveal a hidden mechanism underlying the learning classification inference. The ramification of these findings could lead to a simple yet effective solution to an open-set recognition.