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Future advances in deep learning and its impact on the development ofartificial intelligence (AI) in all fields depends heavily on data size andcomputational power. Sacrificing massive computing resources in exchange forbetter precision rates of the network model is recognized by many researchers.This leads to huge computing consumption and satisfactory results are notalways expected when computing resources are limited. Therefore, it isnecessary to find a balance between resources and model performance to achievesatisfactory results. This article proposes a self-adaptive quality ofexperience (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 thecomputing resources required for training which will allow the experience valueof the model to improve. To maximize the experience value when computerresources are limited, a resource allocation model and solutions need to beestablished. In addition, we carry out experiments based on four network modelsto analyze the experience values with respect to the crowd counting example.Experimental results show that the proposed DeepNetQoE is capable of adaptivelyobtaining a high experience value according to user needs and therefore guidingusers to determine the computational resources allocated to the network models.

DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks