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Posted to dev@singa.apache.org by "wangwei (JIRA)" <ji...@apache.org> on 2018/07/13 06:55:00 UTC
[jira] [Commented] (SINGA-383) Add Separable Convolution for
autograd
[ https://issues.apache.org/jira/browse/SINGA-383?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16542620#comment-16542620 ]
wangwei commented on SINGA-383:
-------------------------------
https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/
> Add Separable Convolution for autograd
> --------------------------------------
>
> Key: SINGA-383
> URL: https://issues.apache.org/jira/browse/SINGA-383
> Project: Singa
> Issue Type: New Feature
> Reporter: wangwei
> Priority: Major
>
> This type of convolution is used in [Xception model|https://arxiv.org/pdf/1610.02357.pdf] and is supported by [other libraries|[https://github.com/pytorch/pytorch/issues/1708].]
>
> To implement it in Singa, we create a new operation (separable_conv_2d) by calling a depthwise_conv_2d (normal convolution with number of output channels=1, and number of groups = number of input channels); and then calling normal convolution with number of groups=1, and kernel size=1, i.e. pointwise convolution.
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