计算机科学
卷积神经网络
稳健性(进化)
冗余(工程)
人工智能
一般化
任务分析
深度学习
任务(项目管理)
多任务学习
网络体系结构
人工神经网络
机器学习
数学分析
操作系统
经济
基因
生物化学
化学
管理
计算机安全
数学
作者
Kejie Lyu,Yingming Li,Zhongfei Zhang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2019-10-04
卷期号:29: 1867-1878
被引量:12
标识
DOI:10.1109/tip.2019.2944522
摘要
Multi-task deep learning methods learn multiple tasks simultaneously and share representations amongst them, so information from related tasks improves learning within one task. The generalization capabilities of the produced models are substantially enhanced. Typical multi-task deep learning models usually share representations of different tasks in lower layers of the network, and separate representations of different tasks in higher layers. However, different groups of tasks always have different requirements for sharing representations, so the required design criterion does not necessarily guarantee that the obtained network architecture is optimal. In addition, most existing methods ignore the redundancy problem and lack the pre-screening process for representations before they are shared. Here, we propose a model called Attention-aware Multi-task Convolutional Neural Network, which automatically learns appropriate sharing through end-to-end training. The attention mechanism is introduced into our architecture to suppress redundant contents contained in the representations. The shortcut connection is adopted to preserve useful information. We evaluate our model by carrying out experiments on different task groups and different datasets. Our model demonstrates an improvement over existing techniques in many experiments, indicating the effectiveness and the robustness of the model. We also demonstrate the importance of attention mechanism and shortcut connection in our model.
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