Closed vadimkantorov closed 7 years ago
I have updated the equation in the paper. Thanks to Vadim Kantorov and Siniša Šegvić. Now the implementation should be consistent with the paper.
Thanks Senthil, the paper was somehow not updated in the vgg website. I think it should be the right version now. Here is the link:
http://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16/bilen16.pdf
On 18/12/16 21:20, Senthil Purushwalkam wrote:
Sorry for reviving this. Can you point me to the new version of the paper? Correct me if I'm wrong. From what I understand, this https://www.robots.ox.ac.uk/%7Evgg/publications/2016/Bilen16/bilen16.pdf and this https://arxiv.org/pdf/1511.02853v3.pdf version still have the issue that @vadimkantorov https://github.com/vadimkantorov pointed out.
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Yes, I think the backward prop uses the previous formulation of the loss #4
Thanks, you are right. I guess if we square probs term, it is gonna be correct, isn't it? This might require some change in the scaling term. I will try to run an experiment with that.
Yep, seems squaring probs in backward pass would make it correct
@vadimkantorov Sorry, could you show me the code after squaring probs in backward pass?
Hi Hakan,
Thanks for putting the code online, it's really a nice and concise code base! I've got two questions concerning the spatial regularizer:
1) As far as I can see in https://github.com/hbilen/WSDDN/blob/master/layers/LossTopBoxSmoothProb.m#L69, you are also putting the
probs
values under the square opposite to the formulation in subsection 3.4 of the paper. Also the gradient computation in https://github.com/hbilen/WSDDN/blob/master/layers/LossTopBoxSmoothProb.m#L85 doesn't square theprobs
value that doesn't correspond to the forward pass code. Am I missing something or is it a bug?2) Do I understand correctly that you differentiate the regularizer only wrt fc7 outputs?
Thanks in advance!