Future advances in deep learning and its impact on the development of artificial intelligence (AI) in all fields depend heavily on data size and computational power. Sacrificing massive computing resources in exchange for better precision rates of the network model is recognized by many researchers. This leads to huge computing consumption, while satisfactory results are not always expected when computing resources are limited. Therefore, it is necessary to find a balance between resources and model performance to achieve satisfactory results. This article proposes a self-adaptive quality of experience (QoE) framework, DeepNetQoE, to guide the training of deep networks. A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve. To maximize the experience value, a resource allocation model and solutions need to be established. Finally, we carry out experiments based on four network models to analyze the experience values with respect to the crowd counting example. Experimental results show that the proposed DeepNetQoE is capable of adaptively obtaining a high experience value according to user requirements and therefore guiding users to determine the computational resources allocated to the network models.

中文翻译：

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DeepNetQoE：深度网络自适应QoE优化框架

深度学习的未来发展及其对所有领域人工智能 (AI) 发展的影响在很大程度上取决于数据规模和计算能力。牺牲大量的计算资源来换取网络模型更好的准确率是很多研究者认可的。这导致巨大的计算消耗，而在计算资源有限的情况下并不总是期望得到令人满意的结果。因此，需要在资源和模型性能之间找到平衡点，才能达到满意的效果。本文提出了一种自适应体验质量 (QoE) 框架 DeepNetQoE，以指导深度网络的训练。建立关联模型的自适应QoE模型' s 的准确性与训练所需的计算资源相匹配，这将使模型的经验值得到提高。为了最大化体验价值，需要建立资源分配模型和解决方案。最后，我们基于四种网络模型进行实验，以分析关于人群计数示例的经验值。实验结果表明，所提出的 DeepNetQoE 能够根据用户需求自适应地获得高体验值，从而引导用户确定分配给网络模型的计算资源。