Metric Learning for Anomaly Detection
Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data.
Yusuf Sarıgöz
·May 04, 2022
Practical use of metric learning for anomaly detection. A way to match the results of a classification-based approach with only ~0.6% of the labeled data.
Yusuf Sarıgöz
·May 04, 2022
What are the advantages of Triplet Loss over Contrastive loss and how to efficiently implement it?
Yusuf Sarıgöz
·March 24, 2022
Discover the power of neural search. Learn what neural search is and follow our tutorial to build a neural search service using BERT, Qdrant, and FastAPI.
Andrey Vasnetsov
·June 10, 2021
Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
Andrei Vasnetsov
·May 15, 2021
How to make ANN search with custom filtering? Search in selected subsets without loosing the results.
Andrei Vasnetsov
·November 24, 2019