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LOGAN: LATENT OPTIMISATION FOR GENERATIVE ADVERSARIAL NETWORKS

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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


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


Christian Mio Loclair: Wow - thats quite a step

1 replies, 10 likes


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


Dave Gershgorn: bird

0 replies, 3 likes


Alistair Young: Is that a pukeko?

0 replies, 1 likes


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


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


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


Content

Found on Dec 03 2019 at https://arxiv.org/pdf/1912.00953.pdf

PDF content of a computer science paper: LOGAN: LATENT OPTIMISATION FOR GENERATIVE ADVERSARIAL NETWORKS