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Oral
Autoregressive Quantile Networks for Generative Modeling
Georg Ostrovski · Will Dabney · Remi Munos
We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression. AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incurring a loss of sample diversity. The method can be applied to many existing models and architectures. In this work we extend the PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using Inception scores, FID, non-cherry-picked samples, and inpainting results. We consistently observe that AIQN yields a highly stable algorithm that improves perceptual quality while maintaining a highly diverse distribution.
Author Information
Georg Ostrovski (DeepMind)
Will Dabney (DeepMind)
Remi Munos (DeepMind)
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2018 Poster: Autoregressive Quantile Networks for Generative Modeling »
Fri. Jul 13th 04:15 -- 07:00 PM Room Hall B #110
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