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Article overview
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SimNets: A Generalization of Convolutional Networks | | Nadav Cohen
; Amnon Shashua
; | | Date: |
3 Oct 2014 | | Abstract: | We present a deep layered architecture that generalizes classical
convolutional neural networks (ConvNets). The architecture, called SimNets, is
driven by two operators, one being a similarity function whose family contains
the convolution operator used in ConvNets, and the other is a new ’soft
max-min-mean’ operator called MMECS that realizes classical operators like ReLU
and max-pooling, but has additional capabilities that make SimNets a powerful
generalization of ConvNets. Two interesting properties that emerge from the
architecture are: (i) the basic input to hidden-units to output-nodes machinery
contains as special case a kernel machine, and (ii) initializing networks using
unsupervised learning is natural. Experiments demonstrate the capability of
achieving state of the art accuracy with networks that are 1/8 the size of
comparable ConvNets. | | Source: | arXiv, 1410.0781 | | Services: | Forum | Review | PDF | Favorites | |
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