Benefiting from the joint learning of the multiple tasks in the deep
multi-task networks, many applications have shown the promising performance
comparing to single-task learning. However, the performance of multi-task
learning framework is highly dependant on the relative weights of the tasks.
How to assign the weight of each task is a critical issue in the multi-task
learning. Instead of tuning the weights manually which is exhausted and
time-consuming, in this paper we propose an approach which can dynamically
adapt the weights of the tasks according to the difficulty for training the
task. Specifically, the proposed method does not introduce the hyperparameters
and the simple structure allows the other multi-task deep learning networks can
easily realize or reproduce this method. We demonstrate our approach for face
recognition with facial expression and facial expression recognition from a
single input image based on a deep multi-task learning Conventional Neural
Networks (CNNs). Both the theoretical analysis and the experimental results
demonstrate the effectiveness of the proposed dynamic multi-task learning
method. This multi-task learning with dynamic weights also boosts of the
performance on the different tasks comparing to the state-of-art methods with
single-task learning.

本文提出了一种基于动态多任务学习的方法，它可以根据训练任务的难度动态调整任务权重，从而实现面部识别和面部表情识别的性能提升，并证明其与单任务学习方法相比具有更高的性能。