Auxiliary Learning by Implicit Differentiation
- URL: http://arxiv.org/abs/2007.02693v3
- Date: Tue, 11 May 2021 06:52:59 GMT
- Title: Auxiliary Learning by Implicit Differentiation
- Authors: Aviv Navon and Idan Achituve and Haggai Maron and Gal Chechik and
Ethan Fetaya
- Abstract summary: Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest.
Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation.
First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function.
Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task.
- Score: 54.92146615836611
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Training neural networks with auxiliary tasks is a common practice for
improving the performance on a main task of interest. Two main challenges arise
in this multi-task learning setting: (i) designing useful auxiliary tasks; and
(ii) combining auxiliary tasks into a single coherent loss. Here, we propose a
novel framework, AuxiLearn, that targets both challenges based on implicit
differentiation. First, when useful auxiliaries are known, we propose learning
a network that combines all losses into a single coherent objective function.
This network can learn non-linear interactions between tasks. Second, when no
useful auxiliary task is known, we describe how to learn a network that
generates a meaningful, novel auxiliary task. We evaluate AuxiLearn in a series
of tasks and domains, including image segmentation and learning with attributes
in the low data regime, and find that it consistently outperforms competing
methods.
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