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In recent years, research on image generation methods has been developingfast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013,which uses variational inference to learn a latent space from the imagedatabase and then generates images using the decoder. The generativeadversarial networks (GANs) came out as a promising framework, which usesadversarial training to improve the generative ability of the generator.However, the generated pictures by GANs are generally blurry. The deepconvolutional generative adversarial networks (DCGANs) were then proposed toleverage the quality of generated images. Since the input noise vectors arerandomly sampled from a Gaussian distribution, the generator has to map from awhole normal distribution to the images. This makes DCGANs unable to reflectthe inherent structure of the training data. In this paper, we propose a noveldeep model, called generative adversarial networks with decoder-encoder outputnoise (DE-GANs), which takes advantage of both the adversarial training and thevariational Bayesain inference to improve the performance of image generation.DE-GANs use a pre-trained decoder-encoder architecture to map the randomGaussian noise vectors to informative ones and pass them to the generator ofthe adversarial networks. Since the decoder-encoder architecture is trained bythe same images as the generators, the output vectors could carry the intrinsicdistribution information of the original images. Moreover, the loss function ofDE-GANs is different from GANs and DCGANs. A hidden-space loss function isadded to the adversarial loss function to enhance the robustness of the model.Extensive empirical results show that DE-GANs can accelerate the convergence ofthe adversarial training process and improve the quality of the generatedimages.

Generative Adversarial Networks with Decoder-Encoder Output Noise