  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
limitation of the traditional loss functions for unsupervised learning of a
given task. Directly using MI for the training purpose is quite challenging to
deal with 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. Due to the unavailability of a standard benchmark, we carefully
design the experimental analysis and select three different tasks for the
experimental study. We demonstrate that L LMI alone provides better gradients
to achieve a neural network better performance over the popular loss functions,
also in the cases when multiple loss functions are used for a given task.
