p-Meta: Towards On-device Deep Model Adaptation

• URL: http://arxiv.org/abs/2206.12705v1
• Date: Sat, 25 Jun 2022 18:36:59 GMT
• Title: p-Meta: Towards On-device Deep Model Adaptation
• Authors: Zhongnan Qu, Zimu Zhou, Yongxin Tong, Lothar Thiele
• Abstract summary: p-Meta is a new meta learning method that enforces structure-wise partial parameter updates while ensuring fast generalization to unseen tasks. We show that p-Meta substantially reduces the peak dynamic memory by a factor of 2.5 on average compared to state-of-the-art few-shot adaptation methods.
• Score: 30.27192953408665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the deployed model on the device with local data. Such an on-device adaption for deep learning empowered applications demands data and memory efficiency. However, existing gradient-based meta learning schemes fail to support memory-efficient adaptation. To this end, we propose p-Meta, a new meta learning method that enforces structure-wise partial parameter updates while ensuring fast generalization to unseen tasks. Evaluations on few-shot image classification and reinforcement learning tasks show that p-Meta not only improves the accuracy but also substantially reduces the peak dynamic memory by a factor of 2.5 on average compared to state-of-the-art few-shot adaptation methods.

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