Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (4): 1-13.doi: 10.11959/j.issn.2096-3750.2022.00306
• Theory and Technology • Next Articles
Huanhuan ZHANG, Anfu ZHOU, Huadong MA
Revised:
2022-10-17
Online:
2022-12-30
Published:
2022-12-01
Supported by:
CLC Number:
Huanhuan ZHANG, Anfu ZHOU, Huadong MA. Reinforcement learning-based real-time video streaming control and on-device training research[J]. Chinese Journal on Internet of Things, 2022, 6(4): 1-13.
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框架名称 | 来源 | 发布年份 | 主要功能 | 特性 |
Core-ML | 苹果公司 | 2017年 | 使用简单;只支持端上推理,经常用于有监督习任务 | 较适用于苹果设备,只支持端上推理;在安卓设备的性能未知 |
ML-Kit | 谷歌公司 | 2018年 | 同时支持苹果和安卓设备,并且可以在两个平台上使用相同的API | 具有6个基本API,易于实现,但只支持有监督学习任务,不支持强化学习的训练 |
Paddle-mobile | 百度公司 | 2019年 | 部署灵活,支持多硬件 | 只支持模型的端上推理,不支持模型的端上训练 |
Caffe2 | Facebook公司 | 2017年 | 同时覆盖训练和推理的通用框架;支持云端深度神经网络的训练 | 只支持模型的端上推理,不支持模型的端上训练 |
TensorFlow Lite | 谷歌公司 | 2018年 | TensorFlow 在移动终端上运行深度学习算法的平台;内存占用较低 | 只支持模型的端上轻量级推理,不支持模型的端上训练 |
TensorFlow.js | 谷歌公司 | 2018年 | TensorFlow 的 JS 平台;灵活,可较好的与Web 交互;同时支持苹果和安卓设备;支持强化学习的端上训练任务 | 同时支持移动终端的推理与训练任务;需要将机器学习相关的低代码重构为JS |
MNN | 阿里巴巴公司 | 2019年 | 轻量级的深度学习端侧推理引擎;同时支持苹果和安卓设备 | 只支持模型的端上推理,不支持模型的端上训练 |
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