A Tree-Structured Multi-Task Model Recommender

Lijun Zhang, Xiao Liu, Hui Guan

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. Our tree-structured multi-task model recommender is open-sourced and available at https://github.com/zhanglijun95/TreeMTL.

Keywords: Multi-Task Learning, Architecture Recommendation Framework

One-sentence Summary: A recommender for automatically suggesting tree-structured multi-task architectures with high task performance while meeting the computational budget.

Track: Main track

Reproducibility Checklist: Yes

Broader Impact Statement: Yes

Paper Availability And License: Yes

Code Of Conduct: Yes

Reviewers:

Lijun Zhang, [e-mail]
Xiao Liu, [e-mail]

CPU Hours: 0

GPU Hours: 0

TPU Hours: 0

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2203.05092/code)