A Tree-Structured Multi-Task Model Recommender

• URL: http://arxiv.org/abs/2203.05092v1
• Date: Thu, 10 Mar 2022 00:09:43 GMT
• Title: A Tree-Structured Multi-Task Model Recommender
• Authors: Lijun Zhang, Xiao Liu, Hui Guan
• Abstract summary: Tree-structured multi-task architectures have been employed to tackle multiple vision tasks in the context of multi-task learning (MTL) This paper proposes a recommender that automatically suggests tree-structured multi-task architectures that could achieve a high task performance while meeting a user-specified computation budget without performing model training. Extensive evaluations on popular MTL benchmarks show that the recommended architectures could achieve competitive task accuracy and computation efficiency compared with state-of-the-art MTL methods.
• Score: 25.445073413243925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tree-structured multi-task architectures have been employed to jointly tackle multiple vision tasks in the context of multi-task learning (MTL). The major challenge is to determine where to branch out for each task given a backbone model to optimize for both task accuracy and computation efficiency. To address the challenge, this paper proposes a recommender that, given a set of tasks and a convolutional neural network-based backbone model, automatically suggests tree-structured multi-task architectures that could achieve a high task performance while meeting a user-specified computation budget without performing model training. Extensive evaluations on popular MTL benchmarks show that the recommended architectures could achieve competitive task accuracy and computation efficiency compared with state-of-the-art MTL methods.

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