论文标题
部分可观测时空混沌系统的无模型预测
p-Meta: Towards On-device Deep Model Adaptation
论文作者
论文摘要
IoT设备收集的数据通常是私人的，并且在各种用户之间都有众多的多样性。因此，学习需要具有可用代表性数据样本的模型预训练，在IoT设备上部署预训练的模型，并使用本地数据在设备上调整已部署的模型。这样的对深度学习授权应用程序的设备改编需要数据和记忆效率。但是，现有的基于梯度的元学习方案无法支持记忆有效的适应性。为此，我们提出了一种新的元学习方法P-META，该方法可以强制执行结构的部分参数更新，同时确保快速概括到看不见的任务。对少量图像分类和增强学习任务的评估表明，与最新的几次改装方法相比，P-META不仅提高了准确性，而且平均将峰值动态记忆降低了2.5倍。
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.
