How Can You Generate Realistic Faces Using GANs?

Generating realistic faces with Generative Adversarial Networks (GANs) is about making fake images that look like real human faces. GANs have two parts. One is the generator and the other is the discriminator. They work against each other to make better images. The generator makes images from random noise. The discriminator checks these images against real ones. This process helps improve the image quality over time.

In this article, we will see how to generate realistic faces using GANs. We cover important topics like the basics of GANs, how to set up your environment, and how to prepare datasets for training. We will look at popular GAN designs made for face generation. We will also give a step-by-step guide for training your GAN. We will talk about how to check the quality of the faces we generate. Plus, we will share practical examples, best tips for making GANs work better, and answer common questions.

  • How to Generate Realistic Faces Using GANs for Your Projects
  • Understanding the Basics of GANs for Face Generation
  • Setting Up Your Environment for Generating Realistic Faces
  • Exploring Popular GAN Architectures for Face Generation
  • Preparing Your Dataset for Training GANs on Faces
  • Training a GAN to Generate Realistic Faces Step by Step
  • Evaluating the Quality of Generated Faces from GANs
  • Practical Examples of Generating Realistic Faces Using GANs
  • Best Practices for Improving GAN Performance in Face Generation
  • Frequently Asked Questions

For more reading on generative AI, we suggest these articles: What is Generative AI and How Does it Work? and How Can You Train a GAN: A Step-by-Step Tutorial Guide. These will give us more understanding about techniques and uses of generative models.

Understanding the Basics of GANs for Face Generation

Generative Adversarial Networks, or GANs, are a type of machine learning tool. They help us create new data that looks like existing data. When we talk about making realistic faces, GANs use two neural networks. These are called the Generator (G) and the Discriminator (D). We train both of them at the same time through something called adversarial training.

Key Concepts:

  • Generator (G): This network takes random noise and makes a synthetic image, like a face. Its job is to create data that seems real.

  • Discriminator (D): This network checks images and tells if they are real, coming from the training set, or fake, made by G. Its main goal is to correctly tell real images from fake ones.

Training Process:

  1. Initialization: We start both G and D with random weights.
  2. Adversarial Training Loop:
    • Step 1: G makes a batch of fake images from random noise.
    • Step 2: We mix these fake images with some real images.
    • Step 3: We train D to get better at telling real images from fake ones.
    • Step 4: We train G to trick D by making it harder for D to tell real from fake images.

Loss Functions:

  • Discriminator Loss (D_loss):

    D_loss = -E[log(D(real_images))] - E[log(1 - D(G(z)))]

  • Generator Loss (G_loss):

    G_loss = -E[log(D(G(z)))]

Practical Implementation:

To use GANs for making faces, we can use libraries like TensorFlow or PyTorch. Here is a simple example using PyTorch to set up the models:

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, noise_dim):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(noise_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, 3 * 64 * 64),  # We make 64x64 RGB images
            nn.Tanh()
        )

    def forward(self, z):
        return self.model(z).view(-1, 3, 64, 64)

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(3 * 64 * 64, 512),
            nn.LeakyReLU(0.2),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, img):
        return self.model(img.view(img.size(0), -1))

Hyperparameters:

  • Noise Dimension: Usually set to 100.
  • Learning Rate: Often set to 0.0002.
  • Batch Size: We should use between 64 and 256.

Learning the basics of GANs is important for making realistic faces. If we want to know more about how to train GANs, we can check this step-by-step tutorial guide on training GANs.

Setting Up Your Environment for Generating Realistic Faces

We want to generate realistic faces using Generative Adversarial Networks (GANs). To do this, we need to set up our environment. This means we will install the right libraries, tools, and frameworks. Here are the steps to help us get started.

System Requirements

  • Operating System: We can use Linux (Ubuntu is better) or Windows
  • Python Version: We need Python 3.6 or newer
  • GPU: An NVIDIA GPU with CUDA support is good for faster training

Required Libraries and Tools

We need to install these libraries using pip:

pip install tensorflow keras numpy matplotlib scipy

For GPU support, let’s make sure we have the right versions of CUDA and cuDNN. We can check our installation with:

python -c "import tensorflow as tf; print(tf.__version__)"

Setting Up Jupyter Notebook

Jupyter Notebook is great for coding and visualization. We should install it with:

pip install notebook

Then, we can start Jupyter Notebook using:

jupyter notebook

Cloning a GAN Example Repository

To start generating faces fast, we can clone an existing GAN example. We can use this command to clone a popular GAN repository:

git clone https://github.com/your-username/generative-faces.git
cd generative-faces

Environment Configuration

It is good to create a virtual environment for managing dependencies separately:

python -m venv gan-env
source gan-env/bin/activate  # On Windows use `gan-env\Scripts\activate`

After that, we can install the needed packages inside the virtual environment.

Example Configuration File

We can use a configuration file (like config.py) for easy hyperparameter management:

# config.py
BATCH_SIZE = 64
EPOCHS = 100
LEARNING_RATE = 0.0002
IMAGE_SIZE = (64, 64)

Testing Your Setup

Let’s create a simple script to check if everything works:

# test_setup.py
import tensorflow as tf

print("TensorFlow version:", tf.__version__)

Now, we run the script:

python test_setup.py

If we see the TensorFlow version printed without any errors, then our environment is ready for generating realistic faces using GANs.

Generative Adversarial Networks (GANs) has changed a lot. Many architectures are now better for creating realistic faces. We can look at some of the most popular GAN architectures for face generation.

  1. Deep Convolutional GAN (DCGAN):

    • Radford and others created DCGAN. It uses convolutional layers instead of fully connected layers.

    • Architecture:

      • Generator: Uses transposed convolutions, Batch Normalization, and ReLU activations.
      • Discriminator: Uses convolutions, Batch Normalization, and Leaky ReLU activations.

    • Code Snippet:

      import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.ConvTranspose2d(100, 256, 4, 1, 0, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),
            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(True),
            nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
            nn.Tanh()
        )

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

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 64, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.model(input)
  2. Progressive Growing GAN (PGGAN):
    • PGGAN has a training method that slowly increases the resolution of generated images.
    • This method makes better quality images and keeps training stable. It starts with low-resolution images and adds layers step by step.
    • It uses instance normalization and pixel normalization techniques.
  3. StyleGAN:

    • StyleGAN comes from NVIDIA. It has a style-based generator that lets us control the style and details of generated images.

    • It has a mapping network that changes a latent vector into another latent space.

    • Code Snippet:

      class StyleGANGenerator(nn.Module):
    def __init__(self):
        super(StyleGANGenerator, self).__init__()
        self.mapping_network = nn.Sequential(
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 512)
        )
        # Define more layers for style-based generation

    def forward(self, z):
        w = self.mapping_network(z)
        # Use w to generate images
  4. CycleGAN:
    • CycleGAN is used for translating images without needing paired examples.
    • We can also use it for face generation, especially for changing current images.
  5. FaceGAN:
    • FaceGAN is made just for making human faces. It often has extra features to make faces look real.
    • It uses attention mechanisms to focus on important features in generation.
  6. BigGAN:
    • BigGAN is a large-scale GAN. It makes better quality images using bigger batch sizes and wider networks.
    • This architecture is good for creating high-resolution faces with more different looks.

Each of these architectures has strong and weak points. The choice depends on what we need for making realistic faces. For more steps on training a GAN, we can check out how to train a GAN - a step-by-step tutorial guide.

Preparing Your Dataset for Training GANs on Faces

To train Generative Adversarial Networks (GANs) for making realistic faces, we need a good dataset. Here are the simple steps to prepare our dataset:

  1. Dataset Selection:

    • We can use existing datasets like CelebA or FFHQ (Flickr-Faces-HQ). These have many different and high-quality images of faces. They are great for GAN training.

  2. Data Collection:

    • If we make our dataset, we should have many kinds of faces. This means different ages, genders, ethnicities, and expressions. We can use web scraping tools or APIs to collect images. But we must follow copyright rules.

  3. Image Preprocessing:

    • First, we resize images to a same size like 128x128 or 256x256 pixels. This keeps things consistent.
    • Next, we normalize pixel values to the range [-1, 1] or [0, 1]. This helps our training go faster.

    Here is a code example for preprocessing:

    from PIL import Image
import numpy as np
import os

def preprocess_image(image_path, size=(128, 128)):
    img = Image.open(image_path)
    img = img.resize(size)
    img_array = np.array(img) / 255.0  # Normalize to [0, 1]
    return img_array

dataset_path = "path/to/your/dataset"
images = []

for filename in os.listdir(dataset_path):
    if filename.endswith(".jpg") or filename.endswith(".png"):
        img_array = preprocess_image(os.path.join(dataset_path, filename))
        images.append(img_array)

images = np.array(images)

  4. Data Augmentation:

    • To make our model stronger, we can use data augmentation. This means we can rotate, flip, and change colors of images. Libraries like TensorFlow and PyTorch help us do this.

    Here is a code example for augmentation:

    from torchvision import transforms

augmentation = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),
    transforms.RandomRotation(10),
    transforms.ToTensor()
])

augmented_images = [augmentation(image) for image in images]

  5. Data Splitting:

    • We need to split our dataset into two parts: training and validation. A common way is to use 80% for training and 20% for validation. This helps us check how well our GAN performs.

  6. Saving the Dataset:

    • Save the preprocessed and augmented images in a good format like TFRecord or HDF5. This makes it easier to load during model training.

  7. Loading the Dataset:

    • We can use data loaders from deep learning frameworks to load images easily during training.

    Here is a code example to define a data loader in PyTorch:

    from torch.utils.data import DataLoader, Dataset

class CustomDataset(Dataset):
    def __init__(self, image_array):
        self.image_array = image_array

    def __len__(self):
        return len(self.image_array)

    def __getitem__(self, idx):
        return self.image_array[idx]

dataset = CustomDataset(augmented_images)
data_loader = DataLoader(dataset, batch_size=64, shuffle=True)

By following these steps, we can have a good dataset ready for training GANs to make realistic faces. This work is very important to get high-quality results in face generation tasks. For more details about training GANs, check this step-by-step tutorial guide.

Training a GAN to Generate Realistic Faces Step by Step

We can train a Generative Adversarial Network (GAN) to create realistic faces by following these simple steps.

  1. Import Libraries: First, we need to import the libraries we need. We can use TensorFlow or PyTorch, based on what we like.

    import tensorflow as tf
from tensorflow.keras import layers
import numpy as np

  2. Load and Prepare the Dataset: We should use a dataset like CelebA or FFHQ for training. We need to preprocess the images by resizing and normalizing them.

    (x_train, _), (_, _) = tf.keras.datasets.cifar10.load_data()
x_train = x_train.astype('float32') / 255.0

  3. Define the GAN Architecture: Next, we create the generator and discriminator models. Here is a simple version.

    def build_generator():
    model = tf.keras.Sequential([
        layers.Dense(256, activation='relu', input_shape=(100,)),
        layers.Dense(512, activation='relu'),
        layers.Dense(1024, activation='relu'),
        layers.Dense(32 * 32 * 3, activation='tanh'),
        layers.Reshape((32, 32, 3))
    ])
    return model

def build_discriminator():
    model = tf.keras.Sequential([
        layers.Flatten(input_shape=(32, 32, 3)),
        layers.Dense(512, activation='relu'),
        layers.Dense(256, activation='relu'),
        layers.Dense(1, activation='sigmoid')
    ])
    return model

  4. Compile the Models: We will use binary cross-entropy as the loss function and an optimizer like Adam.

    generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer='adam')

  5. Training Loop: In the training loop, we alternate between training the discriminator and the generator.

    def train_gan(epochs, batch_size):
    for epoch in range(epochs):
        # Train Discriminator
        idx = np.random.randint(0, x_train.shape[0], batch_size)
        real_images = x_train[idx]
        noise = np.random.normal(0, 1, (batch_size, 100))
        generated_images = generator.predict(noise)

        d_loss_real = discriminator.train_on_batch(real_images, np.ones((batch_size, 1)))
        d_loss_fake = discriminator.train_on_batch(generated_images, np.zeros((batch_size, 1)))

        # Train Generator
        noise = np.random.normal(0, 1, (batch_size, 100))
        g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))

        print(f"{epoch} [D loss: {d_loss_real + d_loss_fake}] [G loss: {g_loss}]")

  6. Generate Images: After we finish training, we can use the generator to create new images.

    noise = np.random.normal(0, 1, (10, 100))
generated_images = generator.predict(noise)

  7. Save the Model: We should save our trained models so we can use them later.

    generator.save('generator_model.h5')
discriminator.save('discriminator_model.h5')

This guide gives us a clear way to train a GAN to make realistic faces. For more detailed info about the GAN design and training, check this guide on training a GAN.

Evaluating the Quality of Generated Faces from GANs

We need to evaluate the quality of faces that Generative Adversarial Networks (GANs) create. This is important to know how well the model works. There are different ways we can assess this. We can use both numbers and our own observations.

Quantitative Metrics

  1. Inception Score (IS):
    • The Inception Score helps us see how good the generated images are. This score comes from a pretrained Inception model.
    • If the score is high, it means the images are good and varied.
    from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing import image
import numpy as np

def inception_score(images, splits=10):
    # Load Inception model
    model = InceptionV3()
    # Preprocess images
    images = [image.img_to_array(img) for img in images]
    preds = model.predict(np.array(images))
    # Calculate IS
    score = ...
    return score
  2. Fréchet Inception Distance (FID):
    • FID helps us compare the generated images with real images. It uses features from the Inception network.
    • Lower FID means the generated images look more like real images.
    from scipy.linalg import sqrtm
def calculate_fid(real_images, fake_images):
    # Calculate mean and covariance for real and fake images
    mu1, sigma1 = real_images.mean(axis=0), np.cov(real_images, rowvar=False)
    mu2, sigma2 = fake_images.mean(axis=0), np.cov(fake_images, rowvar=False)
    fid = np.sum((mu1 - mu2) ** 2) + np.trace(sigma1 + sigma2 - 2 * sqrtm(sigma1 @ sigma2))
    return fid

Qualitative Evaluation

  1. Visual Inspection:
    • We can look at the generated images ourselves to see how real and diverse they are.
    • We can use tools like TensorBoard or Matplotlib to compare the images.
    import matplotlib.pyplot as plt

def visualize_generated_images(images):
    plt.figure(figsize=(10, 10))
    for i in range(10):
        plt.subplot(5, 5, i + 1)
        plt.imshow(images[i])
        plt.axis('off')
    plt.show()
  2. User Studies:
    • We can ask people to rate how real the generated images look.
    • From this feedback, we can learn more about how well the model does.
  3. t-SNE Visualization:
    • We can use t-SNE to show the space of generated images. Then, we can compare it to real images and see the groups and variety.
    from sklearn.manifold import TSNE

def plot_tsne(real_images, fake_images):
    # Combine real and fake images and apply t-SNE
    tsne = TSNE(n_components=2)
    embedded = tsne.fit_transform(np.concatenate((real_images, fake_images)))
    plt.scatter(embedded[:, 0], embedded[:, 1])
    plt.show()

By using these methods, we can evaluate the quality of faces generated by GANs. This helps us make sure they meet the standards we want for our projects. For more details on GAN training and evaluation, we can check out this detailed guide.

Practical Examples of Generating Realistic Faces Using GANs

We can generate realistic faces using Generative Adversarial Networks (GANs). This has become a popular topic in research and applications. Here are some simple examples to show how we can create faces using common GAN models.

Example 1: Using StyleGAN2

StyleGAN2 is a powerful GAN model. It is famous for making high-quality images. Here is a simple way to use it to generate faces:

!pip install stylegan2_pytorch

import torch
from stylegan2_pytorch import Trainer

# Set up the Trainer with details
trainer = Trainer(
    data_dir='path/to/your/dataset',  # Put your dataset path here
    resolution=1024,
    num_gpus=1,
    batch_size=4,
    num_images=1000,
    num_train_steps=10000
)

# Start to train the model
trainer.train()

After we train the model, we can create faces like this:

from stylegan2_pytorch import generate

# Create images
images = generate(trainer, num_images=5)

Example 2: Using Progressive Growing GAN

Progressive Growing GAN is another good model for making high-resolution images.

!pip install tensorflow==1.14

import tensorflow as tf
from tensorflow.keras import layers

# Build the model
def build_progressive_gan():
    model = tf.keras.Sequential()
    model.add(layers.Dense(4*4*512, use_bias=False, input_shape=(100,)))
    model.add(layers.Reshape((4, 4, 512)))
    model.add(layers.Conv2DTranspose(256, kernel_size=5, strides=2, padding='same'))
    # Add more layers step by step...
    return model

# Create faces
model = build_progressive_gan()
noise = tf.random.normal([1, 100])
generated_image = model(noise)

Example 3: Using DCGAN

Deep Convolutional GAN (DCGAN) is a popular way to create images. Here is how we can set it up:

import torch
import torchvision
from torchvision import transforms

# Set up the data loader
transform = transforms.Compose([
    transforms.Resize(64),
    transforms.CenterCrop(64),
    transforms.ToTensor(),
])

dataset = torchvision.datasets.ImageFolder(root='path/to/dataset', transform=transform)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=128, shuffle=True)

# Define the generator and the discriminator
class Generator(torch.nn.Module):
    # Define the model...

class Discriminator(torch.nn.Module):
    # Define the model...

# Training loop
for epoch in range(num_epochs):
    for i, (images, _) in enumerate(dataloader):
        # Train the discriminator and generator...

Example 4: Using CycleGAN for Face-to-Face Translation

CycleGAN helps us change one type of image to another. We can use it to change the style of faces.

!pip install tensorflow-gpu

import tensorflow as tf

# Load the CycleGAN model
model = tf.keras.models.load_model('path/to/cyclegan_model')

# Use the model to change images
img = tf.keras.preprocessing.image.load_img('path/to/image.jpg', target_size=(256, 256))
img = tf.keras.preprocessing.image.img_to_array(img)
img = tf.expand_dims(img, axis=0)

# Create the changed image
transformed_img = model.predict(img)

These examples show basic ways to create realistic faces using different GAN models. We can change the settings and datasets to get better results. For more help on training GANs, we can check out how can you train a GAN: a step-by-step tutorial guide.

Best Practices for Improving GAN Performance in Face Generation

To make Generative Adversarial Networks (GANs) work better for face generation, we can follow some simple best practices.

  • Use Progressive Growing: We should start with low-resolution images. Then we can slowly increase the resolution during training. This helps keep the training stable and makes the generated faces look better.
# Example: Progressive Growing Pseudocode
def progressive_growing_gan(max_resolution):
    for resolution in range(start_resolution, max_resolution, step):
        train_gan_at_resolution(resolution)
  • Incorporate Regularization Techniques: We can use methods like gradient penalty. This helps to keep the training stable.
# Example: Gradient Penalty Calculation
def gradient_penalty(real_images, fake_images, discriminator):
    alpha = torch.rand(real_images.size(0), 1, 1, 1)  # Random interpolation
    interpolated_images = alpha * real_images + (1 - alpha) * fake_images
    interpolated_images.requires_grad_(True)
    
    d_interpolated = discriminator(interpolated_images)
    gradients = autograd.grad(outputs=d_interpolated, inputs=interpolated_images,
                              grad_outputs=torch.ones(d_interpolated.size()).to(device),
                              create_graph=True, retain_graph=True)[0]
    return ((gradients.norm(2, dim=1) - 1) ** 2).mean()
  • Utilize Label Smoothing: To stop the discriminator from being too sure, we can use label smoothing. This helps the GAN learn better.
# Example: Label Smoothing Implementation
real_labels = torch.ones(batch_size) * 0.9  # Smoothing the label from 1 to 0.9
  • Optimize Learning Rates: We should try different learning rates for the generator and discriminator. Often, we use a lower learning rate for the discriminator.
# Example: Setting Different Learning Rates
optimizer_G = optim.Adam(generator.parameters(), lr=0.0002)
optimizer_D = optim.Adam(discriminator.parameters(), lr=0.0001)
  • Apply Data Augmentation: We can make our training data better by using techniques like rotation, flipping, and color changes. This helps the GAN become more strong.
# Example: Data Augmentation with torchvision.transforms
from torchvision import transforms

data_transforms = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
])

  • Use Advanced Architectures: We can try advanced GAN designs like StyleGAN or BigGAN. These are made to create high-quality images.

  • Monitor Training with Metrics: We should check our performance often. We can use metrics like Inception Score (IS) and Fréchet Inception Distance (FID) to see how good the generated faces are.

# Example: Calculate FID
fid_score = calculate_fid(real_images, fake_images)

  • Early Stopping and Checkpointing: We can use early stopping based on validation results and save checkpoints. This helps not to lose our work during training.

  • Increase Training Duration: We should give more time for training, as GANs need a lot of time to learn and make good outputs.

By using these best practices, we can make GANs better at creating realistic faces. This leads to better results in our projects. For more information on GAN training, we can check the guide on how to train a GAN step by step.

Frequently Asked Questions

What are GANs and how do they work for generating realistic faces?

We have Generative Adversarial Networks (GANs). They are made of two neural networks. One is a generator and the other is a discriminator. They work against each other. The generator makes realistic images like faces. The discriminator checks these images. This back-and-forth training helps the generator get better over time. It leads to very realistic face images. If we want to learn more about how neural networks help generative AI, we can read this guide on how do neural networks fuel the capabilities of generative AI.

What are the best GAN architectures for generating realistic faces?

Many GAN architectures are good for making realistic faces. Some of them are Progressive Growing GANs (PGGAN), StyleGAN, and BigGAN. These models use smart methods like feature normalization and adaptive instance normalization. They help create high-quality images. If we want more details on generative models, we can check this article about the key differences between generative and discriminative models.

How do I prepare a dataset for training GANs on faces?

To prepare a dataset for GAN training, we need to collect a lot of good quality face images. We should make sure the dataset is diverse and properly labeled. We can use data augmentation methods like flipping, rotating, or changing colors. These methods can make our dataset more varied. If we want a detailed tutorial on how to train a GAN, we can look at this guide on how can you train a GAN.

What metrics should I use to evaluate the quality of generated faces?

When we want to check the quality of faces made by GANs, we can use some common metrics. These include Inception Score (IS), Fréchet Inception Distance (FID), and visual Turing tests. IS looks at the variety and quality of the images. FID compares real and generated images. These metrics help us see how realistic and diverse the faces are. For examples on how to evaluate GAN results, we can find useful info in this article about real-life applications of generative AI, which is here.

What are some best practices to improve GAN performance in face generation?

We can use different strategies to improve GAN performance for face generation. Some of these are using progressive training, different loss functions, or techniques like spectral normalization. Also, changing the learning rate and batch size can greatly affect training stability and image quality. If we want more detailed tips and best practices, we can read about best practices for using autoencoders in anomaly detection.