  From a service perspective, Multi-Source Domain Adaptation (MSDA) is a
promising scenario to adapt a deployed model to a client's dataset. It can
provide adaptation without a target label and support the case where a source
dataset is constructed from multiple domains. However, it is impractical,
wherein its training heavily relies on prior domain information of the
multi-source dataset -- how many domains exist and the domain label of each
data sample. Moreover, MSDA requires both source and target datasets
simultaneously (physically), causing storage limitations on the client device
or data privacy issues by transferring client data to a server. For a more
practical scenario of model adaptation from a service provider's point of view,
we relax these constraints and present a novel problem scenario of Three-Free
Domain Adaptation, namely TFDA, where 1) target labels, 2) source dataset, and
mostly 3) source domain information (domain labels + the number of domains) are
unavailable. Under the problem scenario, we propose a practical adaptation
framework called FREEDOM. It leverages the power of the generative model,
disentangling data into class and style aspects, where the style is defined as
the class-independent information from the source data and designed with a
nonparametric Bayesian approach. In the adaptation stage, FREEDOM aims to match
the source class distribution with the target's under the philosophy that class
distribution is consistent even if the style is different; after then, only
part of the classification model is deployed as a personalized network. As a
result, FREEDOM achieves state-of-the-art or comparable performance even
without domain information, with reduced final model size on the target side,
independent of the number of source domains.

Machine Learning;Artificial Intelligence