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wgan-gp

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

Model DevelopmentDeep Learning FrameworksPython
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创建于 2017-05-04 · 更新于 2026-10-12 · 今日第 10034 名
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caogang/wgan-gp 排名第 10,034，有 1,547 个星标。

同类别比较

caogang/wgan-gp 排名第 10,034，此处有 8 个同类条目；allenai/bi-att-flow 排名第 10,040。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Deep Learning Frameworks
关键词Image production
星标数1,547
分叉数345
贡献者2
创建日期2017-05-04
更新日期2026-10-12
1 天星标数0
7 天星标数1
排名10,034
上次排名10,053
排名变化19
7 天星标排名9,759
上次 7 天星标排名17,098
7 天星标排名变化7,339
总星标排名7,895
上次总星标排名7,888
总星标排名变化-7
国家China
README 摘录

WGAN-GP An pytorch implementation of Paper "Improved Training of Wasserstein GANs". Prerequisites Python, NumPy, SciPy, Matplotlib A recent NVIDIA GPU **A latest master version of Pytorch** Progress [x] gan_toy. py : Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll). py : Character-level language model (Discriminator is using **nn. py : MNIST (**Running Results while Finished** in 2017. py: 64x64 architectures(**Looking forward to your pull request**) [x] gan_cifar. py: CIFAR-10(**Great thanks to robotcator**) Results Toy Dataset Some

Sample Result, you can refer to the results/toy/ folder for **details**. **8gaussians 154500 iteration** **25gaussians 48500 iteration** **swissroll 69400 iteration** Mnist Dataset Some Sample Result, you can refer to the results/mnist/ folder for **details**. Billion Word Language Generation (Using CNN, character-level) Some Sample Result after 8699 epochs which is detailed in sample I haven't run enough epochs due to that this is very time-comsuming. He moved the mat all out clame t > A fosts of shores

forreuid he pe > It whith Crouchy digcloued defor > Pamreutol the rered in Car inson > Nor op to the lecs ficomens o fe > In is a " nored by of the ot can > The onteon I dees this pirder , > It is Brobes aoracy of " medurn > Rame he reaariod to thim atreast > The stinl who herth of the not t > The witl is f ont UAy Y nalence > It a over , tose sho Leloch Cumm Cifar10 Dataset Some Sample Result, you can refer

to the results/cifar10/ folder for **details**. Acknowledge Based on the implementation igul222/improved_wgan_training and martinarjovsky/WassersteinGAN

常见问题

记录了多少星标和分叉？

caogang/wgan-gp 有 1,547 个星标和 345 个分叉。

仓库排名是多少？

caogang/wgan-gp 排名第 10,034。

记录的语言和类别是什么？

caogang/wgan-gp 有 1,547 个星标，语言为 Python，类别为 Model Development。

哪个仓库排名最接近？

caogang/wgan-gp 与 allenai/bi-att-flow 比较，后者排名第 10,040，有 1,545 个星标。

README

WGAN-GP

An pytorch implementation of Paper "Improved Training of Wasserstein GANs".

Prerequisites

Python, NumPy, SciPy, Matplotlib A recent NVIDIA GPU

A latest master version of Pytorch

Progress

  • [x] gan_toy.py : Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll).(Finished in 2017.5.8)

  • [x] gan_language.py : Character-level language model (Discriminator is using nn.Conv1d. Generator is using nn.Conv1d. Finished in 2017.6.23. Finished in 2017.6.27.)

  • [x] gan_mnist.py : MNIST (Running Results while Finished in 2017.6.26. Discriminator is using nn.Conv1d. Generator is using nn.Conv1d.)

  • [ ] gan_64x64.py: 64x64 architectures(Looking forward to your pull request)

  • [x] gan_cifar.py: CIFAR-10(Great thanks to robotcator)

Results

  • Toy Dataset

    Some Sample Result, you can refer to the results/toy/ folder for details.

    • 8gaussians 154500 iteration

    • 25gaussians 48500 iteration

    • swissroll 69400 iteration

  • Mnist Dataset

    Some Sample Result, you can refer to the results/mnist/ folder for details.

  • Billion Word Language Generation (Using CNN, character-level)

    Some Sample Result after 8699 epochs which is detailed in sample

    I haven't run enough epochs due to that this is very time-comsuming.

    He moved the mat all out clame t

    A fosts of shores forreuid he pe

    It whith Crouchy digcloued defor

    Pamreutol the rered in Car inson

    Nor op to the lecs ficomens o fe

    In is a " nored by of the ot can

    The onteon I dees this pirder ,

    It is Brobes aoracy of " medurn

    Rame he reaariod to thim atreast

    The stinl who herth of the not t

    The witl is f ont UAy Y nalence

    It a over , tose sho Leloch Cumm

  • Cifar10 Dataset

    Some Sample Result, you can refer to the results/cifar10/ folder for details.

Acknowledge

Based on the implementation igul222/improved_wgan_training and martinarjovsky/WassersteinGAN

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