Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering

• URL: http://arxiv.org/abs/2205.15882v1
• Date: Tue, 31 May 2022 15:31:17 GMT
• Title: Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering
• Authors: Jo\~ao Machado de Freitas, Sebastian Berg, Bernhard C. Geiger and Manfred M\"ucke
• Abstract summary: We frame homogeneous-feature multi-task learning as a hierarchical representation learning problem. We assume an additive independent noise model between the task-agnostic and task-specific latent representations. It is shown that our resulting representations yield competitive performance for several MTL benchmarks.
• Score: 5.878411350387833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent representations. Drawing inspiration from the information bottleneck principle and assuming an additive independent noise model between the task-agnostic and task-specific latent representations, we limit the information contained in each task-specific representation. It is shown that our resulting representations yield competitive performance for several MTL benchmarks. Furthermore, for certain setups, we show that the trained parameters of the additive noise model are closely related to the similarity of different tasks. This indicates that our approach yields a task-agnostic representation that is disentangled in the sense that its individual dimensions may be interpretable from a task-specific perspective.

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