This work investigates the task of unsupervised model personalization,
adapted to continually evolving, unlabeled local user images. We consider the
practical scenario where a high capacity server interacts with a myriad of
resource-limited edge devices, imposing strong requirements on scalability and
local data privacy. We aim to address this challenge within the continual
learning paradigm and provide a novel Dual User-Adaptation framework (DUA) to
explore the problem. This framework flexibly disentangles user-adaptation into
model personalization on the server and local data regularization on the user
device, with desirable properties regarding scalability and privacy
constraints. First, on the server, we introduce incremental learning of
task-specific expert models, subsequently aggregated using a concealed
unsupervised user prior. Aggregation avoids retraining, whereas the user prior
conceals sensitive raw user data, and grants unsupervised adaptation. Second,
local user-adaptation incorporates a domain adaptation point of view, adapting
regularizing batch normalization parameters to the user data. We explore
various empirical user configurations with different priors in categories and a
tenfold of transforms for MIT Indoor Scene recognition, and classify numbers in
a combined MNIST and SVHN setup. Extensive experiments yield promising results
for data-driven local adaptation and elicit user priors for server adaptation
to depend on the model rather than user data. Hence, although user-adaptation
remains a challenging open problem, the DUA framework formalizes a principled
foundation for personalizing both on server and user device, while maintaining
privacy and scalability.

本文研究了无监督模型个性化任务，提出了一个新的 Dual User-Adaptation 框架，通过服务器端模型个性化和用户设备上的本地数据正则化，实现了可伸缩性和本地数据隐私保护的用户自适应模型。