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arXiv · 1908.04339

Feature Partitioning for Efficient Multi-Task Architectures

Alejandro Newell·Lu Jiang·Chong Wang·Li-Jia Li·Jia Deng
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Abstract

Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures while taking resource constraints into consideration. We propose a search space that compactly represents different parameter sharing strategies. This provides more effective coverage and sampling of the space of multi-task architectures. We also present a method for quick evaluation of different architectures by using feature distillation. Together these contributions allow us to quickly optimize for efficient multi-task models. We benchmark on Visual Decathlon, demonstrating that we can automatically search for and identify multi-task architectures that effectively make trade-offs between task resource requirements while achieving a high level of final performance.

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Alejandro Newell, Lu Jiang, Chong Wang, Li-Jia Li, Jia Deng. 2019-08-12. Feature Partitioning for Efficient Multi-Task Architectures. https://arxiv.org/abs/1908.04339

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