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On the relationship between disentanglement and multi-task learning

2021-10-07 14:35:34
Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk, Andrzej Bedychaj

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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.03498

PDF

https://arxiv.org/pdf/2110.03498.pdf


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