Pros of adversarial

• Focuses on adversarial examples and robustness, providing a broader scope beyond just GANs
• Includes implementations of various adversarial attack methods
• More actively maintained with recent updates

Cons of adversarial

• Less specialized in GAN improvements compared to improved-gan
• May have a steeper learning curve for those specifically interested in GANs
• Lacks some of the specific GAN enhancements found in improved-gan

Code Comparison

adversarial:

def fgsm(x, predictions, eps, clip_min=None, clip_max=None):
    # Create the graph to compute the sign of the gradient
    grad, = tf.gradients(predictions, x)
    signed_grad = tf.sign(grad)
    # Multiply the sign by epsilon and add to the original input
    adv_x = tf.stop_gradient(x + eps * signed_grad)
    # If clipping is needed, clip the adversarial example
    if (clip_min is not None) and (clip_max is not None):
        adv_x = tf.clip_by_value(adv_x, clip_min, clip_max)
    return adv_x

improved-gan:

def discriminator(x, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        h1 = lrelu(conv2d(x, 64, name='d_h1_conv'))
        h2 = lrelu(d_bn2(conv2d(h1, 128, name='d_h2_conv')))
        h3 = lrelu(d_bn3(conv2d(h2, 256, name='d_h3_conv')))
        h4 = linear(tf.reshape(h3, [batch_size, -1]), 1, 'd_h4_lin')
    return tf.nn.sigmoid(h4), h4

Pros of CycleGAN

• Supports unpaired image-to-image translation, allowing for more flexible dataset requirements
• Implements cycle consistency loss, which helps preserve important characteristics of the input domain
• Provides pre-trained models for various applications, making it easier to get started

Cons of CycleGAN

• Generally more complex architecture and training process compared to Improved-GAN
• May require more computational resources due to the dual generator-discriminator setup
• Can sometimes produce less diverse outputs due to the cycle consistency constraint

Code Comparison

CycleGAN:

def forward(self, input):
    AtoB = self.netG_A(input)
    BtoA = self.netG_B(AtoB)
    return AtoB, BtoA

Improved-GAN:

def forward(self, input):
    return self.netG(input)

The code snippets highlight the key difference in architecture complexity. CycleGAN uses two generators (G_A and G_B) to perform bidirectional translation, while Improved-GAN typically uses a single generator for unidirectional tasks.

Pros of ganhacks

• Comprehensive collection of tips and tricks for training GANs
• Regularly updated with community contributions
• Covers a wide range of GAN-related topics and techniques

Cons of ganhacks

• Less focused on specific implementations or code examples
• May require more effort to implement the suggested techniques

Code comparison

improved-gan:

def generator(z):
    return tf.contrib.layers.fully_connected(z, 784, activation_fn=tf.nn.sigmoid)

def discriminator(x):
    return tf.contrib.layers.fully_connected(x, 1, activation_fn=None)

ganhacks:

# No specific code examples provided in the repository
# The repository focuses on tips and best practices rather than code implementations

Summary

improved-gan is a specific implementation of improved techniques for Generative Adversarial Networks, while ganhacks is a collection of tips and tricks for training GANs. improved-gan provides concrete code examples and implementations, whereas ganhacks offers broader guidance and best practices without specific code implementations. improved-gan may be more suitable for those looking for a ready-to-use implementation, while ganhacks is valuable for developers seeking to improve their understanding and techniques for training GANs across various projects.

Pros of Keras-GAN

• Implements a wide variety of GAN architectures in a single repository
• Uses Keras, making it more accessible for beginners and easier to modify
• Includes pre-trained models and example outputs for quick experimentation

Cons of Keras-GAN

• Less focused on a specific GAN improvement technique
• May not include the latest state-of-the-art GAN architectures
• Potentially less optimized for performance compared to specialized implementations

Code Comparison

Keras-GAN (DCGAN implementation):

def build_generator():
    model = Sequential()
    model.add(Dense(128 * 7 * 7, activation="relu", input_dim=latent_dim))
    model.add(Reshape((7, 7, 128)))
    model.add(UpSampling2D())
    model.add(Conv2D(128, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=0.8))

Improved-GAN:

def Gx(z, is_training):
    output = lib.ops.linear.Linear('Generator.Input', 128, 4*4*4*DIM, z)
    output = tf.reshape(output, [-1, 4*DIM, 4, 4])
    output = ResidualBlock('Generator.Res1', 4*DIM, 4*DIM, 3, output, resample='up', is_training=is_training)
    output = ResidualBlock('Generator.Res2', 4*DIM, 2*DIM, 3, output, resample='up', is_training=is_training)

Pros of DCGAN-tensorflow

• More comprehensive implementation of DCGAN architecture
• Includes pre-trained models and sample outputs
• Better documentation and usage instructions

Cons of DCGAN-tensorflow

• Less focus on improved GAN techniques
• May be more complex for beginners to understand and modify
• Limited to TensorFlow framework

Code Comparison

DCGAN-tensorflow:

def discriminator(image, reuse=False):
    with tf.variable_scope("discriminator") as scope:
        if reuse:
            scope.reuse_variables()
        # Discriminator network implementation

improved-gan:

def Discriminator(x):
    output = lib.ops.linear.Linear('Discriminator.Input', 784, 1024, x)
    output = tf.nn.relu(output)
    output = lib.ops.linear.Linear('Discriminator.2', 1024, 1024, output)
    # More layers...

The DCGAN-tensorflow implementation uses TensorFlow's variable scopes and provides a reuse option, while improved-gan uses a custom linear operation and focuses on a simpler architecture. DCGAN-tensorflow follows the DCGAN paper more closely, whereas improved-gan incorporates various GAN improvements.

Pros of WassersteinGAN

• Improved stability during training compared to Improved-GAN
• Better theoretical foundation for measuring distance between distributions
• More consistent convergence across different architectures and datasets

Cons of WassersteinGAN

• Potentially slower training time due to weight clipping
• May require more careful hyperparameter tuning
• Slightly more complex implementation compared to Improved-GAN

Code Comparison

WassersteinGAN:

def calc_gradient_penalty(netD, real_data, fake_data):
    alpha = torch.rand(BATCH_SIZE, 1)
    interpolates = alpha * real_data + ((1 - alpha) * fake_data)
    disc_interpolates = netD(interpolates)
    gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
                              grad_outputs=torch.ones(disc_interpolates.size()),
                              create_graph=True, retain_graph=True, only_inputs=True)[0]
    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * LAMBDA
    return gradient_penalty

Improved-GAN:

def feature_matching_loss(real_features, fake_features):
    loss = 0
    for r_feat, f_feat in zip(real_features, fake_features):
        loss += torch.mean(torch.abs(r_feat.mean(0) - f_feat.mean(0)))
    return loss

The code snippets highlight the different approaches: WassersteinGAN focuses on gradient penalty calculation, while Improved-GAN emphasizes feature matching loss.