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Deep SimNets | | Nadav Cohen
; Or Sharir
; Amnon Shashua
; | | Date: |
9 Jun 2015 | | Abstract: | We present a deep layered architecture that generalizes convolutional neural
networks (ConvNets). The architecture, called SimNets, is driven by two
operators: (i) a similarity function that generalizes inner-product, and (ii) a
log-mean-exp function called MEX that generalizes maximum and average. The two
operators applied in succession give rise to a standard neuron but in "feature
space". The feature spaces realized by SimNets depend on the choice of the
similarity operator. The simplest setting, which corresponds to a convolution,
realizes the feature space of the Exponential kernel, while other settings
realize feature spaces of more powerful kernels (Generalized Gaussian, which
includes as special cases RBF and Laplacian), or even dynamically learned
feature spaces (Generalized Multiple Kernel Learning). As a result, the SimNet
contains a higher abstraction level compared to a traditional ConvNet. We argue
that enhanced expressiveness is important when the networks are small due to
run-time constraints (such as those imposed by mobile applications). Empirical
evaluation on CIFAR-10 validates the superior expressiveness of SimNets,
showing a significant gain in accuracy over ConvNets when computational
resources at run-time are limited. We also show that in large-scale settings,
where computational complexity is less of a concern, the additional capacity of
SimNets can be controlled with proper regularization, yielding accuracies
comparable to state of the art ConvNets. | | Source: | arXiv, 1506.3059 | | Services: | Forum | Review | PDF | Favorites | |
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