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We present a new method for improving the performances of variationalautoencoder (VAE). In addition to enforcing the deep feature consistentprinciple thus ensuring the VAE output and its corresponding input images tohave similar deep features, we also implement a generative adversarial trainingmechanism to force the VAE to output realistic and natural images. We presentexperimental results to show that the VAE trained with our new methodoutperforms state of the art in generating face images with much clearer andmore natural noses, eyes, teeth, hair textures as well as reasonablebackgrounds. We also show that our method can learn powerful embeddings ofinput face images, which can be used to achieve facial attribute manipulation.Moreover we propose a multi-view feature extraction strategy to extracteffective image representations, which can be used to achieve state of the artperformance in facial attribute prediction.

Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training