Latent cognizance
Pisit Nakjai, Jiradej Ponsawat, Tatpong Katanyukul · 2019
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.