PGGAN | Progressive Growing of GANs for Improved Quality, Stability, and Variation (part 1) | AISC

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Toronto Deep Learning Series, 29 October 2018

Part 2: https://youtu.be/fMds8t_Gt-I

For slides and more information, visit: https://tdls.a-i.science/events/2018-10-22/

Paper: https://arxiv.org/abs/1710.10196

Speaker: Andrew Martin (Logojoy)

Host: Logojoy
Date: Oct 29th, 2018

Progressive Growing of GANs for Improved Quality, Stability, and Variation

We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.



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Tags:
GANs
generative adversarial networks
neural networks
deep learning
ai
machine vision
image processing
progressive gan
pggan