DeDrift: Robust Similarity Search under Content Drift
Dmitry Baranchuk Matthijs Douze Yash Upadhyay I. Zeki Yalniz
The statistical distribution of content uploaded and searched on media
sharing sites changes over time due to seasonal, sociological and technical
factors. We investigate the impact of this "content drift" for large-scale
similarity search tools, based on nearest neighbor search in embedding space.
Unless a costly index reconstruction is performed frequently, content drift
degrades the search accuracy and efficiency. The degradation is especially
severe since, in general, both the query and database distributions change.
  We introduce and analyze real-world image and video datasets for which
temporal information is available over a long time period. Based on the
learnings, we devise DeDrift, a method that updates embedding quantizers to
continuously adapt large-scale indexing structures on-the-fly. DeDrift almost
eliminates the accuracy degradation due to the query and database content drift
while being up to 100x faster than a full index reconstruction.