Dynamic Convolution: Attention over Convolution Kernels

7 Dec 2019Yinpeng ChenXiyang DaiMengchen LiuDongdong ChenLu YuanZicheng Liu

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability. To address this issue, we present Dynamic Convolution, a new design that increases model complexity without increasing the network depth or width... (read more)

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