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May 24, 2023 at 20:13 history edited John St. John CC BY-SA 4.0
added 25 characters in body
Sep 29, 2022 at 7:31 history edited Edoardo Guerriero CC BY-SA 4.0
added 26 characters in body
Jul 5, 2022 at 15:30 comment added John St. John You could do the same process using torch.cdist and the embedding output (before classification layer) of a ResNET or whatever pretrained/frozen image model. You might want to divide each embedded vector by its norm first though so you have a cosine similarity rather than a pure l2 if you do that though. Embeddings from a good image model should be better than emd, but I bet emd is a pretty good baseline.
Jul 5, 2022 at 14:27 comment added John St. John Nice! Glad I could help.
Jul 5, 2022 at 14:18 vote accept Hadar Sharvit
Jul 5, 2022 at 14:18 comment added Hadar Sharvit Wow, this has worked tremendously well. I highly appreciate your response.
Jul 5, 2022 at 13:56 comment added John St. John Or better you could flatten after doing the 3d cumsum in the example above. Then you could use cdist with p=2 to replace the subtract and square steps. You would give the function your list of per-sample flattened cumsum tensors (samples x flat) twice. Then that would be all pairwise distances with something kind of like an EMD.
Jul 5, 2022 at 13:53 comment added John St. John It looks like torch.cdist only supports different values of p for the L_p distance. It doesn’t look like it supports applying a function to all pairs. Sounds like you want to make a kernel matrix? You could use something like pytorch.org/docs/stable/generated/torch.combinations.html and then stack the 0,1 elements from the tuples into two tensors, then run this?
Jul 5, 2022 at 12:11 comment added Hadar Sharvit can this function be used within torch.cdist, to account for pairwise distances of every pair?
Jul 4, 2022 at 22:36 history edited John St. John CC BY-SA 4.0
Added normalization example
Jul 4, 2022 at 20:41 history answered John St. John CC BY-SA 4.0