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Towards Universal Backward-Compatible Representation Learning
[article]
2022
arXiv
pre-print
Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as "backfill"), which is time-consuming and expensive, especially in large-scale applications. The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features. Despite the success, previous works only investigated a
arXiv:2203.01583v2
fatcat:wilqpv2pozflbbicips33nulqm