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The traditional model upgrading paradigm for retrieval requires recomputingall gallery embeddings before deploying the new model (dubbed as"backfilling"), which is quite expensive and time-consuming consideringbillions of instances in industrial applications. BCT presents the first steptowards backward-compatible model upgrades to get rid of backfilling. It isworkable but leaves the new model in a dilemma between new featurediscriminativeness and new-to-old compatibility due to the undifferentiatedcompatibility constraints. In this work, we propose Darwinian Model Upgrades(DMU), which disentangle the inheritance and variation in the model evolvingwith selective backward compatibility and forward adaptation, respectively. Theold-to-new heritable knowledge is measured by old feature discriminativeness,and the gallery features, especially those of poor quality, are evolved in alightweight manner to become more adaptive in the new latent space. Wedemonstrate the superiority of DMU through comprehensive experiments onlarge-scale landmark retrieval and face recognition benchmarks. DMU effectivelyalleviates the new-to-new degradation and improves new-to-old compatibility,rendering a more proper model upgrading paradigm in large-scale retrievalsystems.

Darwinian Model Upgrades: Model Evolving with Selective Compatibility