Learning Compatible Representation

Representation learning underlies visual search, retrieval and recognition by encoding gallery images and matching them with queries. Most early work assumes a fixed model, but new training data or more expressive architectures can change the feature representation. Recomputing every gallery vector after an update is expensive for collections containing billions of images and may be impossible when the original images are unavailable because of privacy or storage restrictions. Compatible representation learning aims to update the model while retaining usable existing gallery features. The talk examines the foundational compatibility problem, the role of representation stationarity, and new training techniques that use stationarity to learn compatible feature representations.