MixBCT: Towards Self-Adapting Backward-Compatible Training
Yu Liang Shiliang Zhang Yaowei Wang Sheng Xiao Kenli Li Xiaoyu Wang
The exponential growth of data, alongside advancements in model structures
and loss functions, has necessitated the enhancement of image retrieval systems
through the utilization of new models with superior feature embeddings.
However, the expensive process of updating the old retrieval database by
replacing embeddings poses a challenge. As a solution, backward-compatible
training can be employed to avoid the necessity of updating old retrieval
datasets. While previous methods achieved backward compatibility by aligning
prototypes of the old model, they often overlooked the distribution of the old
features, thus limiting their effectiveness when the old model's low quality
leads to a weakly discriminative feature distribution. On the other hand,
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. Specifically, we summarize four constraints that
are essential for ensuring backward compatibility in an ideal scenario, and we
construct a single loss function to facilitate backward-compatible training.
Our approach adaptively adjusts the constraint domain for new features based on
the distribution of the old embeddings. 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