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From a service perspective, Multi-Source Domain Adaptation (MSDA) is apromising scenario to adapt a deployed model to a client's dataset. It canprovide adaptation without a target label and support the case where a sourcedataset is constructed from multiple domains. However, it is impractical,wherein its training heavily relies on prior domain information of themulti-source dataset -- how many domains exist and the domain label of eachdata sample. Moreover, MSDA requires both source and target datasetssimultaneously (physically), causing storage limitations on the client deviceor data privacy issues by transferring client data to a server. For a morepractical scenario of model adaptation from a service provider's point of view,we relax these constraints and present a novel problem scenario of Three-FreeDomain Adaptation, namely TFDA, where 1) target labels, 2) source dataset, andmostly 3) source domain information (domain labels + the number of domains) areunavailable. Under the problem scenario, we propose a practical adaptationframework called FREEDOM. It leverages the power of the generative model,disentangling data into class and style aspects, where the style is defined asthe class-independent information from the source data and designed with anonparametric Bayesian approach. In the adaptation stage, FREEDOM aims to matchthe source class distribution with the target's under the philosophy that classdistribution is consistent even if the style is different; after then, onlypart of the classification model is deployed as a personalized network. As aresult, FREEDOM achieves state-of-the-art or comparable performance evenwithout domain information, with reduced final model size on the target side,independent of the number of source domains.

FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization