Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks

The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks. However, there has not been much work on developing an established and systematic way of building the structure and choosing the parameters of a neural network, and this task heavily depends on trial and error and empirical results... (read more)

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