  Achieving backward compatibility when rolling out new models can highly
reduce costs or even bypass feature re-encoding of existing gallery images for
in-production visual retrieval systems. Previous related works usually leverage
losses used in knowledge distillation which can cause performance degradations
or not guarantee compatibility. To address these issues, we propose a general
framework called Learning Compatible Embeddings (LCE) which is applicable for
both cross model compatibility and compatible training in
direct/forward/backward manners. Our compatibility is achieved by aligning
class centers between models directly or via a transformation, and restricting
more compact intra-class distributions for the new model. Experiments are
conducted in extensive scenarios such as changes of training dataset, loss
functions, network architectures as well as feature dimensions, and demonstrate
that LCE efficiently enables model compatibility with marginal sacrifices of
accuracies. The code will be available at https://github.com/IrvingMeng/LCE.
