  Real-world visual search systems involve deployments on multiple platforms
with different computing and storage resources. Deploying a unified model that
suits the minimal-constrain platforms leads to limited accuracy. It is expected
to deploy models with different capacities adapting to the resource
constraints, which requires features extracted by these models to be aligned in
the metric space. The method to achieve feature alignments is called
``compatible learning''. Existing research mainly focuses on the one-to-one
compatible paradigm, which is limited in learning compatibility among multiple
models. We propose a Switchable representation learning Framework with
Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models
with different capacities through one training process. The optimization of
sub-models faces gradients conflict, and we mitigate this problem from the
perspective of the magnitude and direction. We adjust the priorities of
sub-models dynamically through uncertainty estimation to co-optimize sub-models
properly. Besides, the gradients with conflicting directions are projected to
avoid mutual interference. SFSC achieves state-of-the-art performance on the
evaluated datasets.
