  Backward-compatible training circumvents the need for expensive updates to
the old gallery database when deploying an advanced new model in the retrieval
system. Previous methods achieved backward compatibility by aligning prototypes
of the new model with the old one, yet they often overlooked the distribution
of old features, limiting their effectiveness when the low quality of the old
model results in a weakly feature discriminability. Instance-based methods like
L2 regression take into account the distribution of old features but impose
strong constraints on the performance of the new model itself. In this paper,
we propose MixBCT, a simple yet highly effective backward-compatible training
method that serves as a unified framework for old models of varying qualities.
We construct a single loss function applied to mixed old and new features to
facilitate backward-compatible training, which adaptively adjusts the
constraint domain for new features based on the distribution of old features.
We conducted extensive experiments on the large-scale face recognition datasets
MS1Mv3 and IJB-C to verify the effectiveness of our method. The experimental
results clearly demonstrate its superiority over previous methods. Code is
available at https://github.com/yuleung/MixBCT .
