DeepMI: A Mutual Information Based Framework For Unsupervised Deep Learning of Tasks

TL;DR

DeepMI introduces an information-theoretic framework using a linearized mutual information measure to improve unsupervised deep learning, addressing limitations of traditional loss functions and demonstrating superior performance across various tasks.

Contribution

The paper presents a novel MI-based loss function, a fuzzy logic pipeline for integration, and investigates MI's role in unsupervised deep neural network training.

Findings

LLMI provides better gradients than traditional loss functions.

DeepMI improves neural network performance in unsupervised tasks.

The framework is effective across multiple diverse tasks.

Abstract

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.