FastFill: Efficient Compatible Model Update

Understand

In many retrieval systems the original high dimensional data (e.g., images) is mapped to a lower dimensional feature through a learned embedding model.

• The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features.
• When the embedding model is updated, it might produce features that are not comparable/compatible with features already in the gallery computed with the old model.
• Subsequently, all features in the gallery need to be re-computed using the new embedding model -- a computationally expensive process called backfilling.