Multi-Task Learning by a Top-Down Control Network

Hila Levi; Shimon Ullman

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arXiv · 2002.03335

Multi-Task Learning by a Top-Down Control Network

Hila Levi·Shimon Ullman

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Abstract

As the range of tasks performed by a general vision system expands, executing multiple tasks accurately and efficiently in a single network has become an important and still open problem. Recent computer vision approaches address this problem by branching networks, or by a channel-wise modulation of the network feature-maps with task specific vectors. We present a novel architecture that uses a dedicated top-down control network to modify the activation of all the units in the main recognition network in a manner that depends on the selected task, image content, and spatial location. We show the effectiveness of our scheme by achieving significantly better results than alternative state-of-the-art approaches on four datasets. We further demonstrate our advantages in terms of task selectivity, scaling the number of tasks and interpretability.

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Hila Levi, Shimon Ullman. 2020-02-09. Multi-Task Learning by a Top-Down Control Network. https://arxiv.org/abs/2002.03335

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