  In this work, we propose an information theory based framework DeepMI to
train deep neural networks (DNN) using Mutual Information (MI). The DeepMI
framework is especially targeted but not limited to the learning of real world
tasks in an unsupervised manner. The primary motivation behind this work is the
insufficiency of traditional loss functions for unsupervised task learning.
Moreover, directly using MI for the training purpose is quite challenging to
deal because of its unbounded above nature. Hence, we develop an alternative
linearized representation of MI as a part of the framework. Contributions of
this paper are three fold: i) investigation of MI to train deep neural
networks, ii) novel loss function LLMI, and iii) a fuzzy logic based end-to-end
differentiable pipeline to integrate DeepMI into deep learning framework. We
choose a few unsupervised learning tasks for our experimental study. We
demonstrate that L LM I alone provides better gradients to achieve a neural
network better performance over the cases when multiple loss functions are used
for a given task.
