【代码资源】GAN | 七份最热GAN文章及代码分享(Github 1000+Stars)

【导读】专知团队整理了七份当前最热的GAN相关文章和代码,每篇文章代码均在Github上开源,Stars数量超1000+。

编译 | 专知

整理 | Xiaowen

【Title】Progressive Growing of GANs for Improved Quality, Stability, and Variation

【Abstract】We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset.

Paper URL:

http://www.zhuanzhi.ai/document/47a8a0e9bad2c8aeb8449d20c90f2aed

Code URL:

https://github.com/tkarras/progressive_growing_of_gans

【Title】StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

【Abstract】Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.

Paper URL:

http://www.zhuanzhi.ai/document/cbe3065979f77b79a458254c5fd0f027

Code URL:

https://github.com/yunjey/StarGAN

【Title】High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

【Abstract】We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.

Paper URL:

http://www.zhuanzhi.ai/document/cbe3065979f77b79a458254c5fd0f027

Code URL:

https://github.com/NVIDIA/pix2pixHD

【Title】Wasserstein GAN

【Abstract】We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to other distances between distributions.

Paper URL:

http://www.zhuanzhi.ai/document/d7c03277c318ca3f5c0ed56a59cdc7e7

Code URL:

https://github.com/martinarjovsky/WassersteinGAN

【Title】Improved Training of Wasserstein GANs

【Abstract】Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Our proposed method performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning, including 101-layer ResNets and language models over discrete data. We also achieve high quality generations on CIFAR-10 and LSUN bedrooms.

Paper URL:

http://www.zhuanzhi.ai/document/ae335ea911314e19a7e9f4eaf6d1e4a2

Code URL:

https://github.com/igul222/improved_wgan_training

【Title】SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

【Abstract】As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

Paper URL:

http://www.zhuanzhi.ai/document/9f5ca11d8af17a19c279d673f6f34119

Code URL:

https://github.com/LantaoYu/SeqGAN

【Title】StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

【Abstract】Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aimed at generating high-resolution photorealistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

Paper URL:

http://www.zhuanzhi.ai/document/491168aecdf07a7030efc51ce5d13def

Code URL:

https://github.com/hanzhanggit/StackGAN

-END-

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-06-25

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