  Device Model Generalization (DMG) is a practical yet under-investigated
research topic for on-device machine learning applications. It aims to improve
the generalization ability of pre-trained models when deployed on
resource-constrained devices, such as improving the performance of pre-trained
cloud models on smart mobiles. While quite a lot of works have investigated the
data distribution shift across clouds and devices, most of them focus on model
fine-tuning on personalized data for individual devices to facilitate DMG.
Despite their promising, these approaches require on-device re-training, which
is practically infeasible due to the overfitting problem and high time delay
when performing gradient calculation on real-time data. In this paper, we argue
that the computational cost brought by fine-tuning can be rather unnecessary.
We consequently present a novel perspective to improving DMG without increasing
computational cost, i.e., device-specific parameter generation which directly
maps data distribution to parameters. Specifically, we propose an efficient
Device-cloUd collaborative parametErs generaTion framework DUET. DUET is
deployed on a powerful cloud server that only requires the low cost of
forwarding propagation and low time delay of data transmission between the
device and the cloud. By doing so, DUET can rehearse the device-specific model
weight realizations conditioned on the personalized real-time data for an
individual device. Importantly, our DUET elegantly connects the cloud and
device as a 'duet' collaboration, frees the DMG from fine-tuning, and enables a
faster and more accurate DMG paradigm. We conduct an extensive experimental
study of DUET on three public datasets, and the experimental results confirm
our framework's effectiveness and generalisability for different DMG tasks.
