  Future advances in deep learning and its impact on the development of
artificial intelligence (AI) in all fields depends 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 and 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 when computer
resources are limited, a resource allocation model and solutions need to be
established. In addition, 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 needs and therefore guiding
users to determine the computational resources allocated to the network models.
