Issues with Implementation and Replicating Paper Results

[its-sandy]

Hi there! I am working on a PyTorch implementation of SLIDE. I'm currently trying to compare its performance against SLIDE. I'm faced with a few doubts/issues while evaluating SLIDE, and need clarifications for the same.

1. I'm unable to replicate the accuracy vs iteration plot for Delicious 200K dataset using the paramters Simhash, K=9, L=50 mentioned in the paper (plot attached). I also observe that SLIDE's accuracy seems to worsen beyond a certain point. What could be the reasons for these?
   
2. I observe a few inconsistencies in the implementations of WTA and DWTA hashes.

• HashingDeepLearning/SLIDE/LSH.cpp

  Line 82 in 3cebe6f

  index += h<<((_K-1-j)*(int)floor(log(binsize)));

  
  The hashes are combined as index += h<<((_K-1-j)(int)floor(log(binsize))); But, if the hashes are to simply be concatenated, shouldn't it instead be index += h<<((_K-1-j)(int)ceil(log2(binsize))); However, for binsize = 8, I also observe that shifting by floor(log(binsize)) = 2 bits gives better convergence than shifting by ceil(log2(binsize)) = 3 bits. Is this intentional? Why is this the case?

• There appears to be a bug in WTA hash .

  HashingDeepLearning/SLIDE/WtaHash.cpp

  Line 57 in 3cebe6f

  values[i] = data[i*binsize+j];

3. What is the reason behind using simhash for Delicious 200K and DWTA hash for Amazon 670K?
4. The paper had mentioned extension of SLIDE to convolution as a future direction. Has there been any progress along this line?

[Eslam2011]

@its-sandy
Dear Mr. its-dandy,

I want ask you why you are work on PyTorch, and I want ask you if you get the same result in paper or not. I try to run the code along time and always give me Killed.

Also, Are you found weight and savedweight files because I can't find it.
I very need to run the code .