Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent

• URL: http://arxiv.org/abs/2003.10159v1
• Date: Mon, 23 Mar 2020 10:21:44 GMT
• Title: Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent
• Authors: Jonas Prellberg, Oliver Kramer
• Abstract summary: We propose an algorithm to learn the assignment between a shared set of weights and task-specific layers. Learning takes place via a combination of natural evolution strategy and gradient descent. The end result are task-specific networks that share weights but allow independent inference.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In deep multi-task learning, weights of task-specific networks are shared between tasks to improve performance on each single one. Since the question, which weights to share between layers, is difficult to answer, human-designed architectures often share everything but a last task-specific layer. In many cases, this simplistic approach severely limits performance. Instead, we propose an algorithm to learn the assignment between a shared set of weights and task-specific layers. To optimize the non-differentiable assignment and at the same time train the differentiable weights, learning takes place via a combination of natural evolution strategy and stochastic gradient descent. The end result are task-specific networks that share weights but allow independent inference. They achieve lower test errors than baselines and methods from literature on three multi-task learning datasets.

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