Python-MSGGAN多尺度梯度GAN体系结构受ProGAN启发但不使用逐层增长

上传者: 39841848 | 上传时间: 2023-03-02 09:13:03 | 文件大小: 129.7MB | 文件类型: ZIP
MSG-GAN: 多尺度梯度GAN(体系结构受ProGAN启发,但不使用逐层增长)

文件下载

资源详情

[{"title":"( 38 个子文件 129.7MB ) Python-MSGGAN多尺度梯度GAN体系结构受ProGAN启发但不使用逐层增长","children":[{"title":"MSG-GAN-master","children":[{"title":"Gradients_explanation.jpg <span style='color:#111;'> 739.91KB </span>","children":null,"spread":false},{"title":"literature","children":[{"title":"GP-GAN: Gender Preserving GAN for Synthesizing.pdf <span style='color:#111;'> 816.43KB </span>","children":null,"spread":false},{"title":"pix2pix.pdf <span style='color:#111;'> 8.72MB </span>","children":null,"spread":false},{"title":"High-Resolution_Image_Synthesis_and_Semantic_Manipulation_with_Conditional_GANs.pdf <span style='color:#111;'> 4.32MB </span>","children":null,"spread":false},{"title":"Multi-Agent Diverse Generative Adversarial Networks.pdf <span style='color:#111;'> 9.63MB </span>","children":null,"spread":false},{"title":"proGAN.pdf <span style='color:#111;'> 27.20MB </span>","children":null,"spread":false},{"title":"Generative_Multiadversarial_Networks.pdf <span style='color:#111;'> 4.45MB </span>","children":null,"spread":false}],"spread":true},{"title":"diagrams","children":[{"title":"architecture.dia <span style='color:#111;'> 5.15KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE.txt <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"detailed_architecture.jpg <span style='color:#111;'> 284.43KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 7.91KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":".gitignore <span style='color:#111;'> 95B </span>","children":null,"spread":false}],"spread":true},{"title":"architecture.jpeg <span style='color:#111;'> 139.69KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 1.20KB </span>","children":null,"spread":false},{"title":"celebA_samples.png <span style='color:#111;'> 1.53MB </span>","children":null,"spread":false},{"title":"sourcecode","children":[{"title":"latent_space_interpolation.py <span style='color:#111;'> 4.68KB </span>","children":null,"spread":false},{"title":"MSG_GAN","children":[{"title":"GAN.py <span style='color:#111;'> 19.81KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 244B </span>","children":null,"spread":false},{"title":"Losses.py <span style='color:#111;'> 6.46KB </span>","children":null,"spread":false},{"title":"CustomLayers.py <span style='color:#111;'> 7.32KB </span>","children":null,"spread":false}],"spread":true},{"title":"train.py <span style='color:#111;'> 8.16KB </span>","children":null,"spread":false},{"title":"models","children":[{"title":"Celeba","children":[{"title":"1","children":[{"title":"loss.png <span style='color:#111;'> 88.05KB </span>","children":null,"spread":false},{"title":"GAN_DIS_3.pth <span style='color:#111;'> 35.05MB </span>","children":null,"spread":false},{"title":"GAN_GEN_3.pth <span style='color:#111;'> 42.91MB </span>","children":null,"spread":false}],"spread":false},{"title":"3","children":[{"title":"loss.png <span style='color:#111;'> 57.68KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":false},{"title":"data_processing","children":[{"title":"DataLoader.py <span style='color:#111;'> 5.11KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 100B </span>","children":null,"spread":false}],"spread":false},{"title":"demo.py <span style='color:#111;'> 3.49KB </span>","children":null,"spread":false},{"title":"samples","children":[{"title":"video","children":[{"title":"reorderer.py <span style='color:#111;'> 450B </span>","children":null,"spread":false}],"spread":false},{"title":"display_samples","children":[{"title":"32_x_32_sample.jpg <span style='color:#111;'> 13.15KB </span>","children":null,"spread":false},{"title":"64_x_64_sample.jpg <span style='color:#111;'> 19.00KB </span>","children":null,"spread":false},{"title":"4_x_4_sample.jpg <span style='color:#111;'> 4.87KB </span>","children":null,"spread":false},{"title":"sample_diagram.jpg <span style='color:#111;'> 197.39KB </span>","children":null,"spread":false},{"title":"16_x_16_sample.jpg <span style='color:#111;'> 10.71KB </span>","children":null,"spread":false},{"title":"8_x_8_sample.jpg <span style='color:#111;'> 7.30KB </span>","children":null,"spread":false}],"spread":false},{"title":"Celeba","children":[{"title":"1","children":[{"title":"doing_ Work","children":[{"title":"sample_sheet.jpeg <span style='color:#111;'> 855.26KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":false}],"spread":false},{"title":"generate_loss_plots.py <span style='color:#111;'> 2.71KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 54B </span>","children":null,"spread":false}],"spread":true}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明