Keyword Analysis & Research: ttur


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ttur1.610.14295644

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  • [1706.08500] GANs Trained by a Two Time-Scale Update Rule Converge to …

    arxiv.org

    https://arxiv.org/abs/1706.08500

    WEBJun 26, 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.

    DA: 54 PA: 44 MOZ Rank: 19

  • TTUR Explained | Papers With Code

    paperswithcode.com

    https://paperswithcode.com/method/ttur

    WEBTwo Time-scale Update Rule. The 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.

    DA: 50 PA: 95 MOZ Rank: 36

  • Two Time-scale Update Rule

    serp.ai

    https://serp.ai/two-time-scale-update-rule/

    WEBThis is where the Two Time-scale Update Rule (TTUR) comes in. What is TTUR? TTUR is an update rule for GANs that optimizes the learning rate separately for the generator and the discriminator. TTUR is based on the idea that the discriminator converges to a local minimum when the generator is fixed.

    DA: 31 PA: 66 MOZ Rank: 26

  • bioinf-jku/TTUR: Two time-scale update rule for training GANs

    github.com

    https://github.com/bioinf-jku/TTUR

    WEBImproved WGAN (WGAN-GP) implementation forked from https://github.com/igul222/improved_wgan_training with added FID evaluation for the image model and switchable TTUR/orig settings. Lanuage model with JSD Tensorboard logging and switchable TTUR/orig settings.

    DA: 81 PA: 54 MOZ Rank: 99

  • GANs trained by a two time-scale update rule converge to a local …

    acm.org

    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.

    DA: 83 PA: 63 MOZ Rank: 26

  • GANs Trained by a Two Time-Scale Update Rule Converge …

    neurips.cc

    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.

    DA: 34 PA: 24 MOZ Rank: 43

  • Papers with Code - GANs Trained by a Two Time-Scale Update Rule

    paperswithcode.com

    https://paperswithcode.com/paper/gans-trained-by-a-two-time-scale-update-rule

    WEBIn 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.

    DA: 60 PA: 35 MOZ Rank: 44

  • Brief Review — GANs Trained by a Two Time-Scale Update Rule …

    medium.com

    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 · Two Time-scale Update Rule (TTUR) is proposed for GAN training. Fréchet Inception Distance (FID) is also proposed as a metric to measure the generated quality, which is better than Inception Score (IS).

    DA: 36 PA: 84 MOZ Rank: 12

  • Abstract - arXiv.org

    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: 19 PA: 59 MOZ Rank: 61

  • GANs Trained by a Two Time-Scale Update Rule Converge to a …

    neurips.cc

    https://proceedings.neurips.cc/paper_files/paper/2017/hash/8a1d694707eb0fefe65871369074926d-Abstract.html

    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 for both the discriminator and the generator.

    DA: 4 PA: 4 MOZ Rank: 31