GAN(Generative Adversarial Network )

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人脸模型

浏览 113 扫码 分享 2023-11-21 12:51:44
  • 人脸识别
    • VGGFace/VGGface2
    • InsightFace
    • CurricularFace">CurricularFace
  • facenet-pytorch">facenet-pytorch

    人脸识别

    VGGFace/VGGface2

    https://www.robots.ox.ac.uk/~albanie/pytorch-models.html

    InsightFace

    https://github.com/TreB1eN/InsightFace_Pytorch

    CurricularFace

    https://github.com/HuangYG123/CurricularFace

    facenet-pytorch

    https://github.com/timesler/facenet-pytorch

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    • 书签
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    • web image
    • paper
      • Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values
      • GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
      • Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks
    • coures/blog
      • GAN 资料
      • [] book
      • GAN
    • dataset
      • 数据集网站
      • 发丝网站
      • 采集数据
      • Unsplash开源200w 张图片数据集
      • 人脸数据集
    • GAN
      • WASSERSTEIN-BOUNDED GENERATIVE ADVERSARIAL NETWORKS
      • [paper]EigenGAN: Layer-Wise Eigen-Learning for GANs
      • Towards Principled Methods for Training Generative Adversarial Networks
      • Least Squares Generative Adversarial Networks
      • Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
      • [parper]Generative Adversarial Nets
      • PeerGAN: Generative Adversarial Networks with a Competing Peer Discriminator
      • 6 GAN Architectures You Really Should Know
    • metric
      • GAN Metrics
      • GAN LOSS
      • [gan metric] FID
    • Stylegans
      • Analyzing and Improving the Image Quality of StyleGAN
      • A Style-Based Generator Architecture for Generative Adversarial Networks
      • 人脸模型
    • GAN Diversity
      • GAN Diversity
      • Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
      • MSGAN：Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
      • Reliable Fidelity and Diversity Metrics for Generative Models
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