  The task of privacy-preserving model upgrades in image retrieval desires to
reap the benefits of rapidly evolving new models without accessing the raw
gallery images. A pioneering work introduced backward-compatible training,
where the new model can be directly deployed in a backfill-free manner, i.e.,
the new query can be directly compared to the old gallery features. Despite a
possible solution, its improvement in sequential model upgrades is gradually
limited by the fixed and under-quality old gallery embeddings. To this end, we
propose a new model upgrade paradigm, termed Bidirectional Compatible Training
(BiCT), which will upgrade the old gallery embeddings by forward-compatible
training towards the embedding space of the backward-compatible new model. We
conduct comprehensive experiments to verify the prominent improvement by BiCT
and interestingly observe that the inconspicuous loss weight of backward
compatibility actually plays an essential role for both backward and forward
retrieval performance. To summarize, we introduce a new and valuable problem
named privacy-preserving model upgrades, with a proper solution BiCT. Several
intriguing insights are further proposed to get the most out of our method.
