Yes, the slides are a bit difficult to understand on their own, I haven't 
looked at what Caffe is doing with group yet, from the discussion it looks 
like something similar but I could be wrong (and they don't mention the 1 x 
1 convolutions to mix the resulting feature maps). Until I take a closer 
look at it, this lecture by the author has a ~5 minute section where he 
explains the separable convolution slides 
succinctly: https://youtu.be/VhLe-u0M1a8?t=1087

On Wednesday, 24 August 2016 20:45:22 UTC-7, Arjun Jain wrote:
>
> Hi 7VoltCrayon, 
> Thanks for pointing to this grouped convolution. I am not sure if I 
> understood the slide 100%, but to me it sounds similar to caffe's "group 
> by" (https://github.com/BVLC/caffe/issues/778), is that true? If not, can 
> you help me spot the differences?
>
> Thanks a lot. 
>
> On Thu, Aug 25, 2016 at 3:29 AM, 7VoltCrayon <[email protected] 
> <javascript:>> wrote:
>
>> Thank you for the reply. I see that in TensorFlow, this is implemented at 
>> the CUDA level  
>> <https://github.com/tensorflow/tensorflow/blob/d42facc3cc9611f0c9722c81551a7404a0bd3f6b/tensorflow/core/kernels/depthwise_conv_op_gpu.cu.cc>
>>  (linked), 
>> if implementing this in Theano, would it be possible to get a fast 
>> implementation using pre-existing Theano ops? Or is this something that 
>> needs to be done at a C++ / CUDA level?
>>
>> On Wednesday, 24 August 2016 13:48:59 UTC-7, nouiz wrote:
>>>
>>> no, but if someone is interrested, it can be done in Theano too.
>>>
>>> Fred
>>>
>>> On Wed, Aug 24, 2016 at 4:09 PM, 7VoltCrayon <[email protected]> wrote:
>>>
>>>> Does theano have the equivalent of TensorFlow's separable_conv2d function? 
>>>> Where it implements a separable factorized convolution as described in 
>>>> these slides:  
>>>> http://vincent.vanhoucke.com/publications/vanhoucke-iclr14.pdf?attredirects=0
>>>>
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>

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