pranv / arc Goto Github PK

Hi,

Thank you for a paper with clear explanations, that is always appreciated.

I'm implementing your conv-ARC in pytorch. One technical question about this line:
https://github.com/pranv/ARC/blob/master/layers.py#L170

I'm surprised the elementwise multiplication '*' in batched_dot() function works that way. Can you explain the action of batched_dot? In the paper I first understood it to be matrix multiplication, but it is not the case.

As a side question, in the convolutional version, the N X N attention patch is a column across the channels of the convolutional activation, right?

Thank you again.

Hi, could you please provide the version you are using for lasagne and theano
I'm using Theano 0.9 and Lasagne master version but seems incompatible
Thank you.

Great work u/pranv! I was thinking about similar approch, but I have problem to code it correctly (I was trying to make attention of both images at once).
I have some question:

• So you use a hard-attention rather than soft-attention. Any reason?

• In paper you mention that:
  We arrived at the iterative cycling paradigm after trying out many approaches to attend to multiple images at once on a few toy datasets
  Could you list method which fails to learn?

• At repo I see the code related to LFW. Do you have any results on LFW Verification protocol (using net learned on CASIA)?

• I see that for Face Verification you used images 32x32. As I understand, the bigger images are too computational intensive? How long does it take to learn model on CASIA?

Great work. The paper is well written.

This method seems like a natural candidate for object detection, since it comes with the natural capability to focus on a subarea of an image and compare that in isolation to a reference image. I wonder if you or somebody else is already working on that?

Another direction that interests me is to use this method to select "high-confidence samples" and with them the original network could be further improved (comparable to this paper Few-shot Object Detection https://arxiv.org/pdf/1706.08249.pdf). If I understand it correctly the RNN controller is not altered at all by the one-shot examples.