MetaNetwork: A Task-agnostic Network Parameters Generation Framework for Improving Device Model Generalization

• URL: http://arxiv.org/abs/2209.05227v1
• Date: Mon, 12 Sep 2022 13:26:26 GMT
• Title: MetaNetwork: A Task-agnostic Network Parameters Generation Framework for Improving Device Model Generalization
• Authors: Zheqi Lv, Feng Wang, Kun Kuang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Fei Wu
• Abstract summary: We propose a novel task-agnostic framework, named MetaNetwork, for generating adaptive device model parameters from cloud without on-device training. The MetaGenerator is designed to learn a mapping function from samples to model parameters, and it can generate and deliver the adaptive parameters to the device based on samples uploaded from the device to the cloud. The MetaStabilizer aims to reduce the oscillation of the MetaGenerator, accelerate the convergence and improve the model performance during both training and inference.
• Score: 65.02542875281233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying machine learning models on mobile devices has gained increasing attention. To tackle the model generalization problem with the limitations of hardware resources on the device, the device model needs to be lightweight by techniques such as model compression from the cloud model. However, the major obstacle to improve the device model generalization is the distribution shift between the data of cloud and device models, since the data distribution on device model often changes over time (e.g., users might have different preferences in recommendation system). Although real-time fine-tuning and distillation method take this situation into account, these methods require on-device training, which are practically infeasible due to the low computational power and a lack of real-time labeled samples on the device. In this paper, we propose a novel task-agnostic framework, named MetaNetwork, for generating adaptive device model parameters from cloud without on-device training. Specifically, our MetaNetwork is deployed on cloud and consists of MetaGenerator and MetaStabilizer modules. The MetaGenerator is designed to learn a mapping function from samples to model parameters, and it can generate and deliver the adaptive parameters to the device based on samples uploaded from the device to the cloud. The MetaStabilizer aims to reduce the oscillation of the MetaGenerator, accelerate the convergence and improve the model performance during both training and inference. We evaluate our method on two tasks with three datasets. Extensive experiments show that MetaNetwork can achieve competitive performances in different modalities.

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