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LOGAN: LATENT OPTIMISATION FOR GENERATIVE ADVERSARIAL NETWORKS

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Dec 03 2019 DeepMind

We introduce LOGAN, a game-theory motivated algorithm, which improves the state-of-the-art in GAN image generation by over 30% measured in FID: https://arxiv.org/abs/1912.00953 Here are samples showing higher diversity: https://t.co/GkdRofrYRt
13 replies, 1089 likes


Dec 03 2019 roadrunner01

LOGAN: Latent Optimisation for Generative Adversarial Networks pdf: https://arxiv.org/pdf/1912.00953.pdf abs: https://arxiv.org/abs/1912.00953 https://t.co/etEhxjOw34
2 replies, 72 likes


Dec 05 2019 Christian Mio Loclair

Wow - thats quite a step
1 replies, 10 likes


Feb 18 2020 Sander Dieleman

@fhuszar Not sure if it's been mentioned, but LOGAN (https://arxiv.org/abs/1912.00953) is a recent practical example.
0 replies, 6 likes


Dec 03 2019 Dave Gershgorn

bird
0 replies, 3 likes


Dec 03 2019 Elliot Turner

Great quality and diversity-of-samples improvement from LOGAN (from Google) compared to BigGAN-deep (LOGAN is on the right in my pasted image) - https://arxiv.org/pdf/1912.00953.pdf https://t.co/lmPYlJDwwC
0 replies, 1 likes


Dec 03 2019 Alistair Young

Is that a pukeko?
0 replies, 1 likes


Dec 04 2019 akira

https://arxiv.org/abs/1912.00953 Improve GAN performance by optimizing latent variables z and greatly update ImageNet SOTA. D and G are updated using z' that is optimized z by D value. Optimizing latent variables with natural gradient is more effective. https://t.co/qbEXcaeTPR
0 replies, 1 likes


Dec 04 2019 Brundage Bot

LOGAN: Latent Optimisation for Generative Adversarial Networks. Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, and Timothy Lillicrap http://arxiv.org/abs/1912.00953
1 replies, 0 likes


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