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Collection of generative models in Tensorflow
| Date | Stars |
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| 2026-07-24 | 3919 |
| 2026-07-25 | 3919 |
| 2026-07-28 | 3919 |
| 2026-07-30 | 3919 |
| 2026-08-06 | 3919 |
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# tensorflow-generative-model-collections Tensorflow implementation of various GANs and VAEs. ## Related Repositories ### Pytorch version Pytorch version of this repository is availabel at https://github.com/znxlwm/pytorch-generative-model-collections ### "Are GANs Created Equal? A Large-Scale Study" Paper https://github.com/google/compare_gan is the code that was used in [the paper](https://arxiv.org/abs/1711.10337). It provides IS/FID and rich experimental results for all gan-variants. ## Generative Adversarial Networks (GANs) ### Lists *Name* | *Paper Link* | *Value Function* :---: | :---: | :--- | **GAN** | [Arxiv](https://arxiv.org/abs/1406.2661) | <img src = 'assets/equations/GAN.png' height = '70px'> **LSGAN**| [Arxiv](https://arxiv.org/abs/1611.04076) | <img src = 'assets/equations/LSGAN.png' height = '70px'> **WGAN**| [Arxiv](https://arxiv.org/abs/1701.07875) | <img src = 'assets/equations/WGAN.png' height = '105px'> **WGAN_GP**| [Arxiv](https://arxiv.org/abs/1704.00028) | <img src = 'assets/equations/WGAN_GP.png' height = '70px'> **DRAGAN**| [Arxiv](https://arxiv.org/abs/1705.07215) | <img src = 'assets/equations/DRAGAN.png' height = '70px'> **CGAN**| [Arxiv](https://arxiv.org/abs/1411.1784) | <img src = 'assets/equations/CGAN.png' height = '70px'> **infoGAN**| [Arxiv](https://arxiv.org/abs/1606.03657) | <img src = 'assets/equations/infoGAN.png' height = '70px'> **ACGAN**| [Arxiv](https://arxiv.org/abs/1610.09585) | <img src = 'assets/equations/ACGAN.png' height = '70px'> **EBGAN**| [Arxiv](https://arxiv.org/abs/1609.03126) | <img src = 'assets/equations/EBGAN.png' height = '70px'> **BEGAN**| [Arxiv](https://arxiv.org/abs/1702.08431) | <img src = 'assets/equations/BEGAN.png' height = '105px'> #### Variants of GAN structure <img src = 'assets/etc/GAN_structure.png' height = '600px'> ### Results for mnist Network architecture of generator and discriminator is the exaclty sames as in [infoGAN paper](https://arxiv.org/abs/1606.03657). For fair comparison of core ideas in all gan variants, all implementations for network architecture are kept same except EBGAN and BEGAN. Small modification is made for EBGAN/BEGAN, since those adopt auto-encoder strucutre for discriminator. But I tried to keep the capacity of discirminator. The following results can be reproduced with command: ``` python main.py --dataset mnist --gan_type <TYPE> --epoch 25 --batch_size 64 ``` #### Random generation All results are randomly sampled. *Name* | *Epoch 2* | *Epoch 10* | *Epoch 25* :---: | :---: | :---: | :---: | GAN | <img src = 'assets/mnist_results/random_generation/GAN_epoch001_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/GAN_epoch009_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/GAN_epoch024_test_all_classes.png' height = '230px'> LSGAN | <img src = 'assets/mnist_results/random_generation/LSGAN_epoch001_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/LSGAN_epoch009_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/LSGAN_epoch024_test_all_classes.png' height = '230px'> WGAN | <img src = 'assets/mnist_results/random_generation/WGAN_epoch001_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/WGAN_epoch009_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/WGAN_epoch024_test_all_classes.png' height = '230px'> WGAN_GP | <img src = 'assets/mnist_results/random_generation/WGAN_GP_epoch001_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/WGAN_GP_epoch009_test_all_classes.png' height = '230px'> | <img src = 'assets/mnist_results/random_generation/WGAN_GP_epoch024_test_all_classes.png' height = '230px'> DRAGAN | <img src = 'assets/mnist_results/random_generation/DRAGAN_epoch001_test_all_classes.png' height = '230px'> | <img
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NAVER Clova AI Research
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Jisung Hwang · United States
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Minjae Kim · NCSOFT
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ce156d3906eb44ef, topic:tensorflow
matched fp:ce156d3906eb44ef, topic:gan