Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies

• URL: http://arxiv.org/abs/2303.06856v1
• Date: Mon, 13 Mar 2023 05:01:50 GMT
• Title: Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies
• Authors: Wonhyeok Choi, Sunghoon Im
• Abstract summary: We present a new MTL framework that searches for optimized structures for multiple tasks with diverse graph topologies. We design a restricted DAG-based central network with read-in/read-out layers to build topologically diverse task-adaptive structures.
• Score: 14.574399133024594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a restricted DAG-based central network with read-in/read-out layers to build topologically diverse task-adaptive structures while limiting search space and time. We search for a single optimized network that serves as multiple task adaptive sub-networks using our three-stage training process. To make the network compact and discretized, we propose a flow-based reduction algorithm and a squeeze loss used in the training process. We evaluate our optimized network on various public MTL datasets and show ours achieves state-of-the-art performance. An extensive ablation study experimentally validates the effectiveness of the sub-module and schemes in our framework.

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