{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","id":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","uuid":"cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title":"智慧的疆界 从图灵机到人工智能","normalized_title":"智慧的疆界从图灵机到人工智能","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787111778004","isbns":["9787111778004","9787111695714"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":456,"navigation_url":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","title_url":"https://workshop.closeclaw.top/ai/title/%E6%99%BA%E6%85%A7%E7%9A%84%E7%96%86%E7%95%8C%20%E4%BB%8E%E5%9B%BE%E7%81%B5%E6%9C%BA%E5%88%B0%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb","page_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cf76bc49-7e9b-4f42-bee8-3050a9fb9feb/search"},"page":{"printed_page":320,"pdf_page":335,"physical_index":335,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"da7bb3cbf51c39914d947182a4e598663d713fce43e24b6e6a71a94c5c4f507f"},"chapter":"内容简介","content":"2018 年，英伟达把过去几年在生成式对抗网络上的研究成果进行整合，发布了 GAN 领域的集大成者—StyleGAN 模型（包括后来分别在 2019 年、2020 年改进的 StyleGAN2 和 StyleGAN3）。StyleGAN 不仅能够生成高质量、高分辨率的图片，还能够改变图片风格、对生成的结果进行精细化控制，譬如通过参数调整人物的动作、形态、肤色、表情、发型等各种特征。\n\n8.4 学者与荣耀\n\n2014 年，经过学术界和工业界几年时间的消化与酝酿，来到了深度学习在生成式领域迸发的临界点。生成式对抗网络诞生的所有前置条件都已经成熟，笔者认为即便没有那天晚上酒吧里古德费洛的灵光一现，生成式对抗网络也一定会在不久的将来换个作者、换个名字登上历史舞台。此观点可以通过现今最成功的三种图像生成式模型都是在 2014 年这个时间点前后同时被发现获得证明。除了生成式对抗网络以外，另外两种模型是 2013 年由迪德利克·金马（Diederik Kingma）提出的变分自编码器（Variational Auto Encoder，VAE）以及 2015 年由雅沙·索尔迪克斯坦（Jascha Sohl-Dickstein）提出的扩散模型（Diffusion Model）<sup>○一</sup>。\n\n320\n\n第三部分 第三波高潮","previous_page":319,"next_page":321}