Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches

TL;DR

This paper systematically compares concept-based explanations and disentanglement learning, highlighting their similarities, differences, limitations, and trade-offs across various tasks in interpretable machine learning.

Contribution

It provides a comprehensive overview and comparison of concept-based and disentanglement approaches, revealing their respective strengths and weaknesses.

Findings

Both approaches can be data inefficient.

They are sensitive to task specifics.

Their effectiveness depends on the concept representation.

Abstract

Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement learning literature has focused on extracting similar representations in an unsupervised or weakly-supervised way, using deep generative models. Despite the overlapping goals and potential synergies, to our knowledge, there has not yet been a systematic comparison of the limitations and trade-offs between concept-based explanations and disentanglement approaches. In this paper, we give an overview of these fields, comparing and contrasting their properties and behaviours on a diverse set of tasks, and highlighting their potential strengths and limitations. In particular, we demonstrate that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.