A Jittor implementation of Conditional GAN (CGAN)

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• A Jittor implementation of Conditional GAN (CGAN)
  • 🌈 概述
  • 🔧 环境配置
  • 🔗 参考

🌈 概述

基于Jittor框架实现CGAN。CGAN的核心思路是将噪声与标签一起投入生成器，将真实图片与标签一起投入判别器，从而使生成器可以生成指定类别的图片。由于MLP与CNN都可以对图像信息进行提取，所以分别基于二者实现CGAN（基于CNN实现的CGAN也可以看作是DCGAN的变种）。实现的两种模型分别在MNIST与CIFAR10上进行训练和测试。

🔧 环境配置

• python 3.10.4

• 安装Jittor

  # 检查python版本大于等于3.8
  python --version
  conda install pywin32
  python -m pip install jittor
  python -m jittor.test.test_core
  python -m jittor.test.test_example
  python -m jittor.test.test_cudnn_op

🔗 参考

• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarival Networks
• An empirical study on evaluation metrics of generative adversarial networks

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