jeanfeydy
commented
3 years ago Hi @Chen-Cai-OSU ,
Thanks for your interest in the library! Indeed, this is a problem that we have worked on quite a bit: in our KNN benchmarks, we refer to MIPS as to "KNN search with a cosine similarity". (Note that we tend to normalize vectors in these benchmarks, but this has no influence on run times for bruteforce implementations.)
To answer your questions:
- Yes, KeOps is fully compatible with deep learning in PyTorch.
- Currently, bruteforce KeOps implementations are extremely competitive for KNN and MIPS on data sets that have less than 1M samples in dimension D<50, as well as non-Euclidean metrics.
- Between January and May, I have worked with an excellent group of students at Imperial College to start adding support for non-bruteforce methods in KeOps. Their code is available in the pull request #143 and provides an equivalent to the FAISS "IVF-Flat" strategy for KNN/MIPS with any metric: I will merge it into the KeOps master branch over the summer.
What do you think? Best regards, Jean
Chen-Cai-OSU
Hello,
Thank you very much for the amazing work. I really like extensive experiments in your paper. I was wondering have you ever looked at MIPS? https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html
It's also a very important problem and I am curious that the performance compared to other solutions such as scann and faiss. I am interested in both brute force and none brute-force approaches, just like KNN in your paper.
I also want to confirm one thing with you:
Thank you!