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Channel fusion locally reduces the feature dimension by replacing the redundant channel pair with a single channel, suppressing the map distance between the two models. It is applicable to network model switching such as pruning hidden layer units and reducing input channels. Effective map distance ...
Frymoyer, Edward J.; " Fibre Channel Fusion: Low Latency, High Speed "; Hewlett-Packard Co., Feb. 1995; http://www.data.com/Tutorials/Fibre-Channel-Fusion.html; Nov. 25, 1996; pp. 1-9.E.M.Frymoyer.“Fiber Channel Fusion:Low Latency, High Speed”. Data Communications . 1995...
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Attention-based multi-channel feature fusion enhancement network to process low-light images IET image processing 中科院4区影响因子2.3 提出了一种基于注意力的多通道特征融合增强网络(M-FFENet)来处理低光图像。 ·首先使用特征提取模型来获得下采样的低光图像的深层特征,并将其拟合到仿射双边网格。
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【论文泛读】Deep Multimodal Fusion by Channel Exchanging,论文题目:DeepMultimodalFusionbyChannelExchanging时间:2020来
由于深度学习的成功,目前所指的多模态融合大多数都是基于端到端的神经网络[38]。对于现有的这些方法,按照如何进行多模态融合的方式,可以分为以下几种:基于聚合的融合方式(aggregation-based fusion)和基于对齐的融合方式(alignment-based),以及将两种方法进行结合的方式[4]。
在过去的几十年中,潜在因子模型(LF)已广泛用于构建推荐系统。越来越多的研究人员一直在尝试利用深度神经网络来学习高级RS的潜在表示。继主流的基于深度学习的RS以来,我们提出了一种新颖的深度融合模型(Deep Fusion Model,DFM),旨在提高深度RS中的表示学习能力,可用于候选检索和项目重新排序。