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View Code? Open in Web Editor NEWOfficial Implementation of Convolutional Normalization: Improving Robustness and Training for Deep Neural Networks
Official Implementation of Convolutional Normalization: Improving Robustness and Training for Deep Neural Networks
Hi,
Thanks for providing the complete code for the ConvNorm paper, my question is about replacing the Conv2d operations with ConvNorm using 1d kernel size eg. (1, 16).
For a kernel length smaller than the input, the V tensor will have smaller dimension which will raise an error in the multiplication (V*cout). After checking the code I think the error is due to the absence of zero padding for the kernel as in the case of 2D kernels.
I think this work is good. But please note that a similar idea behind the work has recently been explored -- we think the computations are in fact easier in the spatial domain. We appreciate if you could kindly discuss and cite our work.
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