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[1706.08500] GANs Trained by a Two Time-Scale Update Rule Converge to …
https://arxiv.org/abs/1706.08500
webJun 26, 2017 · In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark. Comments: Implementations are available at: this https URL. Subjects:
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TTUR Explained | Papers With Code
https://paperswithcode.com/method/ttur
webThe Two Time-scale Update Rule (TTUR) is an update rule for generative adversarial networks trained with stochastic gradient descent. TTUR has an individual learning rate for both the discriminator and the generator.
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bioinf-jku/TTUR: Two time-scale update rule for training GANs
https://github.com/bioinf-jku/TTUR
webTwo time-scale update rule for training GANs. This repository contains code accompanying the paper GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.
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GANs trained by a two time-scale update rule converge to a local …
https://dl.acm.org/doi/10.5555/3295222.3295408
webDec 4, 2017 · We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator.
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Brief Review — GANs Trained by a Two Time-Scale Update Rule …
https://sh-tsang.medium.com/brief-review-gans-trained-by-a-two-time-scale-update-rule-converge-to-a-local-nash-equilibrium-73635435538
webAug 2, 2023 · Figure 1: Left: Original vs. TTUR GAN training on CelebA. TTUR has an individual learning rate for both the discriminator and the generator, which is better than using same learning rate situations as above. And GANs can converge to a local Nash equilibrium when trained by a TTUR, i.e., when discriminator and generator have separate learning rates.
DA: 64 PA: 26 MOZ Rank: 68
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GANs Trained by a Two Time-Scale Update Rule Converge …
https://proceedings.neurips.cc/paper/2017/file/8a1d694707eb0fefe65871369074926d-Paper.pdf
webupdate rule (TTUR) for training GANs with stochastic gradient descent on ar-bitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium.
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Papers with Code - GANs Trained by a Two Time-Scale Update Rule …
https://paperswithcode.com/paper/gans-trained-by-a-two-time-scale-update-rule
webHowever, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate …
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Abstract - arXiv.org
https://arxiv.org/pdf/1706.08500.pdf
webupdate rule (TTUR) for training GANs with stochastic gradient descent on ar-bitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium.
DA: 23 PA: 41 MOZ Rank: 16
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[2201.11989] Existence and Estimation of Critical Batch Size for
https://arxiv.org/abs/2201.11989
webJan 28, 2022 · Previous results have shown that a two time-scale update rule (TTUR) using different learning rates, such as different constant rates or different decaying rates, is useful for training generative adversarial networks (GANs) in theory and in practice.
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GANs Trained by a Two Time-Scale Update Rule Converge to a
https://proceedings.neurips.cc/paper_files/paper/2017/hash/8a1d694707eb0fefe65871369074926d-Abstract.html
webWe propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator.
DA: 59 PA: 88 MOZ Rank: 78