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Question on dropping function #25
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Hi, During testing, we explicit shuffle the feature channels. However, during training, we implicitly shuffle the feature channels by choosing to drop certain kernels. These are equivalent mathematically. |
@ShichenLiu Thanks for your reply, I still have the following two questions: (2) self._mask[i::self.groups, d, :, :].fill_(0) Thanks a lot |
Hi, I have one question on function dropping in layers.py.
I don't understand why learned group convolution still needs the shuffling operation?
https://github.com/ShichenLiu/CondenseNet/blob/master/layers.py#L78
I notice there is a shuffle operation mentioned in 4.1's first graph:
"we permute the output channels of the first 1x1_conv learned group convolution layer,
such that the features generated by each of its groups are evenly used by all the groups of
the subsequent 3x3 group convolutional layer"
However, this operation aims to shuffle feature maps, not convolutional kernels.
Can you explain a little bit?
Thanks in advance
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