Misc,
End-to-End Multi-Task Learning with Attention
(Mar 28, 2018)
Abstract
In this paper, we propose a novel multi-task learning architecture, which
incorporates recent advances in attention mechanisms. Our approach, the
Multi-Task Attention Network (MTAN), consists of a single shared network
containing a global feature pool, together with task-specific soft-attention
modules, which are trainable in an end-to-end manner. These attention modules
allow for learning of task-specific features from the global pool, whilst
simultaneously allowing for features to be shared across different tasks. The
architecture can be built upon any feed-forward neural network, is simple to
implement, and is parameter efficient. Experiments on the CityScapes dataset
show that our method outperforms several baselines in both single-task and
multi-task learning, and is also more robust to the various weighting schemes
in the multi-task loss function. We further explore the effectiveness of our
method through experiments over a range of task complexities, and show how our
method scales well with task complexity compared to baselines.
