GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼科技大学电气与数据工程学院）
Jiyang Xie, Dongliang Chang, Zhanyu Ma, Guoqiang Zhang, Jun Guo
Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0,1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are available at https://github.com/PRIS-CV/GPCA.
OnePlanet Research Center（OnePlanet研究中心）；imec-the Netherlands（荷兰微电子研究中心（imec荷兰分部））；Google Research（谷歌研究院）；Donders Institute for Brain, Cognition and Behaviour（唐德斯大脑、认知与行为研究所）；Radboud University（拉德堡德大学）
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University of Science and Technology of China（中国科学技术大学）；University of Amsterdam（阿姆斯特丹大学）
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Ruhr University Bochum（波鸿鲁尔大学）；Bucknell University（巴克内尔大学）
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Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
National Institute of Technology, Rourkela, India（印度国立理工学院鲁尔克拉分校）；Poverty Alleviation Research Centre, National Institute of Technology, Rourkela, India（印度国立理工学院鲁尔克拉分校扶贫研究中心）
A computationally lightweight safe learning algorithm
Aalto University（阿尔托大学）；Uppsala University（乌普萨拉大学）；Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）；Eindhoven University of Technology（埃因霍温理工大学）
Quantum Artificial Intelligence Laboratory, NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心量子人工智能实验室）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；Cambridge Quantum Computing Limited（剑桥量子计算有限公司）；SGT Inc.（SGT公司）；Instituto de Matemáticas Aplicadas, Universidad de Cartagena（卡塔赫纳大学应用数学系）；Exploration Technology Directorate, NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心探索技术部）；USRA Research Institute for Advanced Computer Science（美国航天研究所高级计算机科学研究所）
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Siemens Corporate Technology（西门子企业技术）；Microsoft Research Asia（微软亚洲研究院）
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