On the relationship between disentanglement and multi-task learning

• URL: http://arxiv.org/abs/2110.03498v1
• Date: Thu, 7 Oct 2021 14:35:34 GMT
• Title: On the relationship between disentanglement and multi-task learning
• Authors: {\L}ukasz Maziarka, Aleksandra Nowak, Maciej Wo{\l}czyk, Andrzej Bedychaj
• Abstract summary: We take a closer look at the relationship between disentanglement and multi-task learning based on hard parameter sharing. We show that disentanglement appears naturally during the process of multi-task neural network training.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks. At the same time finding a joint, adaptable representation of data is one of the key challenges in the multi-task learning setting. In this paper, we take a closer look at the relationship between disentanglement and multi-task learning based on hard parameter sharing. We perform a thorough empirical study of the representations obtained by neural networks trained on automatically generated supervised tasks. Using a set of standard metrics we show that disentanglement appears naturally during the process of multi-task neural network training.

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