主题生成、icon生成、GAN学习

hosts: kaggle,github , arxiv

#arxiv.org
128.84.21.199 arxiv.org

#kaggle hosts
35.244.233.98  kaggle.com

#github hosts from https://raw.githubusercontent.com/521xueweihan/GitHub520/main/hosts
#20220218
# GitHub520 Host Start
140.82.114.25                 alive.github.com
140.82.113.26                 live.github.com
185.199.108.154               github.githubassets.com
140.82.113.22                 central.github.com
185.199.108.133               desktop.githubusercontent.com
185.199.108.153               assets-cdn.github.com
185.199.108.133               camo.githubusercontent.com
185.199.108.133               github.map.fastly.net
199.232.69.194                github.global.ssl.fastly.net
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185.199.108.153               github.io
140.82.114.3                  github.com
192.0.66.2                    github.blog
140.82.113.5                  api.github.com
185.199.108.133               raw.githubusercontent.com
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140.82.112.10                 codeload.github.com
52.217.136.177                github-cloud.s3.amazonaws.com
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52.217.173.73                 github-production-release-asset-2e65be.s3.amazonaws.com
52.217.160.81                 github-production-user-asset-6210df.s3.amazonaws.com
52.217.227.161                github-production-repository-file-5c1aeb.s3.amazonaws.com
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23.100.27.125                 github.dev
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theme generate study

李宏毅机器学习笔记(LeeML-Notes)

《动手学深度学习》

theme generate related

Iconify 2004.03179

IconTransfer 图标生成技术预研

GAN

李宏毅深度学习笔记—GAN (第三方整理 感觉不全)

GAN理解

GAN 李宏毅 笔记 重要图:

固定G, 此时网络结构只有D, 只训练D固定D，此时网络结构为G->D, 其中D相当于损失函数(D的输出相当于损失函数, D的输出是优化目标), 只训练G

不好的GAN代码举例

VAE二维可视化例子分析

VAE.py
utils.py
tensorflow-GANs 首页的二维散点图

GAN paper : Image Translation Image-to-Image Translation with Conditional Adversarial Networks

Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

Unsupervised Image-to-Image Translation Networks

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

pix2pixHD

XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

Toward Multimodal Image-to-Image Translation

BicycleGAN

Multimodal Unsupervised Image-to-Image Translation

MUNIt

Video-to-Video Synthesis

paper_vid2vid

vid2vid

Everybody Dance Now

pytorch-EverybodyDanceNow

Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation

Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

SelectionGAN

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

LGGAN

StarGAN v2: Diverse Image Synthesis for Multiple Domains

stargan-v2

Structural-analogy from a Single Image Pair

structural-analogy

High-Resolution Daytime Translation Without Domain Labels

HiDT

Appendix: Rethinking the Truly Unsupervised Image-to-Image Translation

tunit

Diverse Image Generation via Self-Conditioned GANs

self-conditioned-gan

Contrastive Learning for Unpaired Image-to-Image Translation

contrastive-unpaired-translation

GAN paper : Facial Attribute Manipulation Autoencoding beyond pixels using a learned similarity metric

vae-gan-tensorflow

Coupled Generative Adversarial Networks

CoGAN-tensorflow

Learning Residual Images for Face Attribute Manipulation

FaceAttributeManipulation

NEURAL PHOTO EDITING WITH INTROSPECTIVE ADVERSARIAL NETWORKS

Neural-Photo-Editor