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In [15, 16], the articles designed a document image classification using convolutional neural network (CNN) that shares weights among neurons among a layer.
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The Cambridge Public School District shares weight screening classifications with the students' caregivers by mail, together with locally available resources.
A CNN relies on three concepts: local receptive fields, shared weights, and spatial subsampling [15].
The Siamese tracking network and the Siamese region proposal network share weights which are trained end-to-end.
LG-CNN is jointly trained by two stream loss functions to guide the updating of the shared weights.
Fusion strategy (F): The early and the late fusion are different in the way of sharing weights.
CNNs could achieve some degree of shift and deformation invariance by using local receptive fields, shared weights, and spatial subsampling.
Another sharing scheme known as variable-sharing was also considered in preliminary experiments, where the amount of shared weights to each expert was dependent of their individual losses.
Furthermore, we design a back propagation algorithm with shared weights learned from a softmax layer to update the pretrained parameters of multiple stacked autoencoders simultaneously.
In particular, the shared weights are supportive for improving the generalization of CNNs, because they reduce the number of parameters of CNNs.
CNNs provide shift, scale, and distortion invariance to some extent through use of local receptive fields, shared weights, and spatial or temporal sub-sampling [45].
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