Found the wiki article more useful in describing what Similarity Learning and Metric Learning are: https://en.wikipedia.org/wiki/Similarity_learning
Great article. I've been working in and around this space since 2014, and I think similarity learning, vector search, and embedding management will be a core part of future applications that leverage ML.
I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is a huge part of it, and I'm glad more and more tools like Quaterion are being released to help make this kind of tech ubiquitous.
Is it somehow connected to Qdrant similarity search engine? Is there a default integration for it?
I’m familiar with metric learning within the Mahalanobis family for kNN oriented applications . I’m not getting what use cases this framework targets? Is it custom image search type stuff which may benefit from fine tuning?
What is a realistic minimum viable dataset for an approach like this? When is it not advisable? How does it compare to other more basic approaches?
Very cool. Can you comment on how this compares with tensorflow similarity? https://blog.tensorflow.org/2021/09/introducing-tensorflow-s...
I realise this is an overly-broad question, but any insight into what's the state-of-art in Similarity Learning for article-type text?
More specifically, I'm interested in deriving distances between writing style, arguing style, etc.
There is one https://github.com/jina-ai/finetuner pretty well-designed and also gives SOTA performance from its docs
The title is written in a clickbait format. So I had to point it out.
Not a full featured framework, but pytorch-metric-learning has data loaders, lossess, etc. to facilitate similarity learning: https://github.com/KevinMusgrave/pytorch-metric-learning
Disclaimer: I've made some contributions to it.