生成对抗网络 (Generative Adversarial Networks)

GAN简介 (Introduction to GANs)

生成对抗网络（GAN）是一种深度学习模型，通过生成器和判别器的对抗学习来生成逼真的数据。

Generative Adversarial Networks (GANs) are deep learning models that generate realistic data through adversarial learning between a generator and a discriminator.

基本架构 (Basic Architecture)

生成器 (Generator)

• 网络结构 (Network Structure)
• 潜在空间 (Latent Space)
• 上采样技术 (Upsampling Techniques)

判别器 (Discriminator)

• 分类任务 (Classification Task)
• 特征提取 (Feature Extraction)
• 损失函数 (Loss Functions)

GAN变体 (GAN Variants)

DCGAN (Deep Convolutional GAN)

• 卷积架构 (Convolutional Architecture)
• 稳定训练 (Stable Training)
• 应用场景 (Applications)

条件GAN (Conditional GAN)

• 条件嵌入 (Condition Embedding)
• 类别控制 (Class Control)
• 属性编辑 (Attribute Editing)

CycleGAN

• 循环一致性 (Cycle Consistency)
• 无配对数据 (Unpaired Data)
• 风格迁移 (Style Transfer)

实践应用 (Practical Applications)

图像生成 (Image Generation)

import tensorflow as tf

# 生成器模型 (Generator model)
def make_generator_model():
    model = tf.keras.Sequential([
        # 开始从随机噪声生成 (Start from random noise)
        tf.keras.layers.Dense(7*7*256, use_bias=False, input_shape=(100,)),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.LeakyReLU(),
        tf.keras.layers.Reshape((7, 7, 256)),
        
        # 上采样层 (Upsampling layers)
        tf.keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.LeakyReLU(),
        
        tf.keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same'),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.LeakyReLU(),
        
        # 输出层 (Output layer)
        tf.keras.layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', activation='tanh')
    ])
    return model

# 判别器模型 (Discriminator model)
def make_discriminator_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=[28, 28, 3]),
        tf.keras.layers.LeakyReLU(),
        tf.keras.layers.Dropout(0.3),
        
        tf.keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'),
        tf.keras.layers.LeakyReLU(),
        tf.keras.layers.Dropout(0.3),
        
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(1)
    ])
    return model

图像编辑 (Image Editing)

• 风格迁移 (Style Transfer)
• 图像修复 (Image Inpainting)
• 超分辨率 (Super Resolution)

数据增强 (Data Augmentation)

• 样本生成 (Sample Generation)
• 域适应 (Domain Adaptation)
• 类别平衡 (Class Balancing)

高级技巧 (Advanced Techniques)

训练稳定性 (Training Stability)

• 梯度惩罚 (Gradient Penalty)
• 谱归一化 (Spectral Normalization)
• 渐进式增长 (Progressive Growing)

评估指标 (Evaluation Metrics)

• Inception Score
• FID Score
• 多样性度量 (Diversity Metrics)

实战项目 (Hands-on Project)

在下一节中，我们将实现一个基于DCGAN的图像生成系统，学习如何训练和优化GAN模型。

In the next section, we will implement an image generation system based on DCGAN, learning how to train and optimize GAN models.