Conditional Image Generation with PixelCNN Decoders

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), pp. 4797-4805, 2016.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

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 dive...More

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