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Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord · Nal Kalchbrenner · Lasse Espeholt · koray kavukcuoglu · Oriol Vinyals · Alex Graves

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #39

This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.

Author Information

Aaron van den Oord (Google Deepmind)
Nal Kalchbrenner (Google Brain)
Lasse Espeholt (Google Brain Amsterdam)
koray kavukcuoglu (Google DeepMind)
Oriol Vinyals (DeepMind)

Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.

Alex Graves (Google DeepMind)

Main contributions to neural networks include the Connectionist Temporal Classification training algorithm (widely used for speech, handwriting and gesture recognition, e.g. by Google voice search), a type of differentiable attention for RNNs (originally for handwriting generation, now a standard tool in computer vision, machine translation and elsewhere), stochastic gradient variational inference, and Neural Turing Machines. He works at Google Deep Mind.

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