# Multi-Task Learning by a Top-Down Control Network

arXiv: https://arxiv.org/abs/2002.03335 · HTML: https://mlsanity.com/paper/2002.03335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Image interpretation by iterative bottom-up top-down processing](https://mlsanity.com/paper/2105.05592/) (2021)

## Abstract (arXiv)

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.