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

• URL: http://arxiv.org/abs/2104.06917v1
• Date: Wed, 14 Apr 2021 15:06:34 GMT
• Title: Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
• Authors: Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre, Mateja Jamnik, Pietro Li\`o, Adrian Weller
• Abstract summary: We give an overview of concept-based explanations and disentanglement approaches. We show 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.
• Score: 24.786152654589067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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