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Aligngan learning to align cross domain images with conditional generative adversarial networks
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AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks

M2M-GAN: Many-to-Many Generative Adversarial Transfer Learning for Person Re-Identification | DeepAI

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Realized by adversarial training with additional ability to exploit domain-specific information, the proposed network ...

There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years. Proposed in 2014, GAN has been applied to various ...

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Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN ...

Explosive growth — All the named GAN variants cumulatively since 2014. Credit: Bruno Gavranović

Least Squares Generative Adversarial Networks

Generative Adversarial Networks

Least Squares Generative Adversarial Networks

Generative Adversarial Networks

Pretty painting is always better than a Terminator. Every week, new papers on Generative Adversarial Networks ...

Image translation is a burgeoning field in computer vision where the goal is to learn the mapping between an input image and an output image.

Haoran Xie | BEng, MSc, PhD | The Education University of Hong Kong, Hong Kong | ied | Department of Mathematics and Information Technology (MIT)

Generative Adversarial Networks ...

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Generative Adversarial Networks (GANs)

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Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, ...

GitHub - GKalliatakis/Delving-deep-into-GANs: Generative Adversarial Networks (GANs) resources sorted by citations

Generative Adversarial Networks 20 ...

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Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from ...

Generative Adversarial Networks

coGAN【10】/align gan【11】可以在两个domain不存在对应样本的情况下学出一个联合分布,方法是每一个domain 使用一个GAN,并且将高层的语义信息进行强制权值共享。

(PDF) MMSEdiscoGAN_GOOGLEMLSLP.pdf | Mihir Parmar - Academia.edu

Least Squares Generative Adversarial Networks

Jquery实时改变网页设计(背景 ...

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack ...

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... 使用gulp-concat合并js文件 ...

Generative Adversarial Networks

Computer Vision – ECCV 2018 Workshops

2.1 基本原理

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There has also been increased interest in disentangling the internal representations learned by ...

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Xudong Mao

Generative Adversarial Networks

TP-GAN【15】,用于人脸正脸化。

... 64.  Goodfellow, Ian, et al. "Generative adversarial nets.

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Figure 4. Generated results on face dataset.

1.2 生成式模型

Generative Adversarial Networks

条件GAN的出现,使得控制GAN的输出有了可能,出现了例如文本生成图像【6】的应用。

... Network Architecture  LAPGAN  Stacked GAN; 19.  NIPS 2016 Tutorial: Generative Adversarial ...

直接從原始論文中截取偽代碼了,可見,就是採用判別式模型和生成式模型分別循環依次迭代的方法,與CNN一樣,使用梯度下降來優化。

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However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built ...

生成对抗网络Generative Adversarial Network

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All-About-the-GAN/README-one.md at master · hollobit/All-About-the-GAN · GitHub

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TP-GAN【15】,用于人脸正脸化。

Generative Adversarial Networks

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文【27】提出了一种去雨的算法,很有实际意义。

Figure 3. Generated results on digit datasets. (a): Digits and edge

Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by ...

srgan【31】是最早使用GAN做超分辨重建的應用,它將輸入從隨機雜訊改為低解析度的圖片,使用了殘差結構和perception loss,有很大的應用價值。

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DesignGan【14】,用於設計T恤。

2 Materials and Methods CGAN is composed of three neural network structures, which are a

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Osindero, Conditional generative adversarial nets, arxiv preprint arxiv:1411.1784, 2014. [

Generative Adversarial Networks

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當然,本文還有一些解決model collapse 的trick也很有意思,我會在第二部分里討論。這篇文章對我的啟發很大,順著他的引用,我也粗看了一些近幾年來「多層次GAN」的 ...

In this paper, we study the problem of multi-domain image generation, the goal of which is to generate pairs of corresponding images from different domains.

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在这样的基础上,有一些很有意义的应用。比如苹果simGAN【12】用于优化仿真数据的方案,此时生成器G的输入是合成图像,而不是随机向量,它完美学习到了人工合成 ...

文【24】学习了一个数据集到另一个数据集的迁移,可以用于迁移学习,如实现漫画风格。

上面是一個分類例子,可知判別式模型,有清晰的分界面,而生成式模型,有清晰的概率密度分布。生成式模型,可以轉換為判別式模型,反之則不能。

Xudong Mao 毛旭东

pytorch

ProGAN: How NVIDIA Generated Images of Unprecedented Quality

Qing Li at Independent Researcher

Better Loss Function Colors in ab space (continuous) Regression with L2 loss

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目前來說,GAN主要是做樣本生成吧,我前幾天復現了WGAN-GP以及DCGAN的模型,數據集是在mnist進行測試效果圖一般吧(只是模型復現了一下,與實驗結果還是有差距。

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably the revolutionary techniques are in the area of ...

Generative Adversarial Networks

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