Overview of Conditional Generative Models (Conditional Generative Models) and Examples of Implementation

Conditional Generative Models

Conditional Generative Models, a type of generative model, is a model that has the ability to generate data given certain conditions. Conditional Generative Models play an important role in many application fields because they can generate data based on given conditions.

Algorithm used in the conditional generative model

Various algorithms are used in conditional generative models. Typical algorithms and their characteristics are described below.

• Conditional GANs (Conditional GANs): Conditional GANs add conditions to regular GANs described in “Overview of GANs and their various applications and implementations“, introducing conditions between the generator and the discriminator. The generator takes random noise and conditions as input and generates conditional data based on them. The discriminator receives conditions and data to distinguish between real and generated data, and conditional GANs are used for tasks such as image generation, image repair, and style transformation.
• Conditional Variational Autoencoder (VAE): Conditional VAEs introduce conditions into Variational Autoencoder (VAE)described in “Overview of Variational Autoencoder (VAE), its algorithms and implementation examples“: VAEs learn a latent space representation of data and generate data through a generator and encoder, and conditional VAEs adds conditions to the encoder and decoder and has the ability to generate data for specific conditions. This is used for text-to-image generation and speech synthesis.
• Pix2Pix: Pix2Pix will be a type of conditional GAN specialized for image-to-image conversion tasks. For example, converting a map to an aerial photograph or a black-and-white image to a color image. Pix2Pix is trained using pairs of input and output images as training data.
• CycleGAN: CycleGAN can be a conditional GAN for image transformations between different domains; CycleGAN simultaneously learns transformations from domain A to domain B and vice versa from domain B to domain A, building models that can perform both transformations.
• StackGAN: StackGAN will be a conditional GAN that generates images sequentially from text. It uses multiple generators and discriminators to generate detailed images in a stepwise fashion and is used to generate detailed images such as photographs from text.

These algorithms are designed for different aspects and tasks of the conditional generation model and are used in diverse applications. Each algorithm has its own unique features and advantages, and the selection of the appropriate algorithm should be tailored to the specific task.

Application Examples

Conditional generative models have been widely applied in a variety of domains. The following are some of the major applications of conditional generative models.

• Image Generation:

It is possible to generate images based on specific conditions or classes. For example, it can generate a corresponding image from a given text.

• Image Repair:

A conditional generative model is used to restore a damaged image to its original state. Noise and missing parts can be complemented to restore the original image.

• Image Style Conversion:

Conditional generative models are used to apply a particular image style to another image. An example would be a style transformation that applies the style of a famous painting to a photograph.

• Text to Image Generation:

A conditional generative model is used to generate an image based on a textual description. An example is the task of generating a landscape image from text.

• Speech Synthesis:

Conditional generative models are used to generate speech based on specific speech characteristics or speaker voices. Examples include text-to-speech, where a specific text is read out loud by a specific speaker’s voice.

• Face Generation:

New face images can be generated based on specific criteria (e.g., gender, age, emotion, etc.) Some studies use GANs to generate realistic human face images.

• Video Generation:

Video can be generated based on specific conditions or scene settings. For example, animated characters can be generated to perform specified actions.

• Data Extension:

To increase the size of a dataset, new data can be generated using conditional generative models on existing data. This is expected to improve the generalization performance of the model.

The following sections describe each of these algorithms in detail and provide specific implementation examples.

Conditional GANs

<Overview>

Conditional GANs (cGANs) add conditional information to regular GANs, resulting in a generative model with the ability to generate data based on specific conditions. cGANs consist of two networks, a generator and a discriminator. the generator takes conditions and random noise as inputs and based on them It generates conditioned data. The discriminator, on the other hand, takes condition and data to distinguish between real and generated data. These features allow cGAN to adjust the generated data to meet specific conditions, making it a useful method in many applications.

The training process for cGAN will train both the generator and discriminator as in a regular GAN, but with the addition of providing condition information to the generator. The condition information will influence the generative process as the generator receives conditions and noise and outputs the generated data. The discriminator receives data and condition information to distinguish between real and generated data, and is updated in a manner similar to the learning process for a regular GAN.

Conditional GANs are used for many tasks such as image generation, image repair, and image transformation. Some examples of applications are shown below.

• Conditional Image Generation: Based on specific conditions (e.g., class label or text description), generate images that match a specific class or content. For example, an image can be generated that corresponds to a specific text description.
• Image Repair: Used to restore a damaged image to its original state. A pair of damaged and original images is provided as a condition, and the generator repairs the damaged areas and restores the original image.
• Image Conversion: Used to convert an image with a particular image style or attribute to another image, for example, a daytime landscape image can be converted to an evening landscape image.
• Segmentation Map Generation: Used to generate a segmentation map for a particular image, for example, a semantic segmentation map can be generated for a given image.

Conditional GAN is applied in many aspects of data generation and can be a technique that provides the ability to control the generated data through conditional information.

<Algorithm>

Conditional GAN is an algorithm for controlling the generation capability of a regular GAN by adding conditional information. cGAN consists of two networks: a generator (Generator) and a discriminator (Discriminator). The basic steps of the conditional GAN algorithm are described below.

1. Model Building:
   • Generator: The network in charge of generating conditional data. It usually receives conditional information and random noise as input and generates data based on the conditions. The generated data is trained to resemble the real data.
   • Discriminator: This network is responsible for discriminating between the generated data and the real data. It receives conditional information and data as input and determines whether the data is generated or real.
2. The training process:
   • Data Preparation: The training data is prepared by pairing real data with the corresponding conditional information.
   • Generator training: The generator receives random noise and condition information as input and generates data based on the conditions. The discriminator learns to distinguish the generated data from the real data.
   • Discriminator Training: The discriminator is trained to distinguish between real data and generated data. When identifying the generated data, the corresponding conditional information is also provided.
3. Loss Function:
   • Generator Loss: The goal of the generator is to ensure that the discriminator cannot distinguish the generated data from the real data. The generator is trained by minimizing the loss of reversing the identification result of the generated data.
   • Discriminator Loss: The discriminator is trained to correctly discriminate between real data and generated data. The discriminator loss is computed by considering the discrimination results for the real data and the discrimination results for the generated data, respectively.
4. Alternating training:
   • The generator and discriminator are trained alternately. Each time the generator generates data, the discriminator is also updated with the generated data, and through this process, the generator evolves to generate higher quality data and the discriminator evolves to discriminate more accurately.

<Implementation>

This section describes a simple example implementation of a conditional GAN. The example here is a Python implementation using TensorFlow. The actual project requires detailed adjustments and hyperparameters, but the following shows the basic flow.

First, import the necessary libraries.

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Flatten, Reshape
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

Next, the generator and discriminator models are defined.

def build_generator(input_dim, condition_dim):
    input_layer = Input(shape=(input_dim,))
    condition_layer = Input(shape=(condition_dim,))
    combined_inputs = tf.keras.layers.concatenate([input_layer, condition_layer])

    x = Dense(128)(combined_inputs)
    x = Dense(256)(x)
    output_layer = Dense(output_dim, activation='tanh')(x)

    return Model(inputs=[input_layer, condition_layer], outputs=output_layer)

def build_discriminator(input_dim, condition_dim):
    input_layer = Input(shape=(input_dim,))
    condition_layer = Input(shape=(condition_dim,))
    combined_inputs = tf.keras.layers.concatenate([input_layer, condition_layer])

    x = Dense(256)(combined_inputs)
    x = Dense(128)(x)
    output_layer = Dense(1, activation='sigmoid')(x)

    return Model(inputs=[input_layer, condition_layer], outputs=output_layer)

Once the generator and discriminator models are defined, compile each.

input_dim = ...  # Number of input dimensions
condition_dim = ...  # Conditional dimension number
output_dim = ...  # Number of output dimensions

generator = build_generator(input_dim, condition_dim)
generator.compile(optimizer=Adam(0.0002, 0.5), loss='binary_crossentropy')

discriminator = build_discriminator(input_dim, condition_dim)
discriminator.compile(optimizer=Adam(0.0002, 0.5), loss='binary_crossentropy', metrics=['accuracy'])

Next, the conditional GAN assembly and training loop is defined.

discriminator.trainable = False
input_layer = Input(shape=(input_dim,))
condition_layer = Input(shape=(condition_dim,))
generated_data = generator([input_layer, condition_layer])
validity = discriminator([generated_data, condition_layer])

cGAN = Model(inputs=[input_layer, condition_layer], outputs=validity)
cGAN.compile(optimizer=Adam(0.0002, 0.5), loss='binary_crossentropy')

# training loop
epochs = ...
batch_size = ...

for epoch in range(epochs):
    for _ in range(steps_per_epoch):
        batch_real_data, batch_conditions = ...  # Batch retrieval of real data and conditions
        batch_fake_data = generator.predict([batch_real_data, batch_conditions])

        real_labels = np.ones((batch_size, 1))
        fake_labels = np.zeros((batch_size, 1))

        d_loss_real = discriminator.train_on_batch([batch_real_data, batch_conditions], real_labels)
        d_loss_fake = discriminator.train_on_batch([batch_fake_data, batch_conditions], fake_labels)

        g_loss = cGAN.train_on_batch([batch_real_data, batch_conditions], real_labels)

    # Added process to store and evaluate data generated per epoch

Conditional Variational Autoencoders

Conditional VAE (cVAE) is a generative model that incorporates conditional information into Variational Autoencoders (VAEs) to provide the ability to generate data based on specific conditions. cVAE consists of an Encoder, a Decoder, and a network containing conditional information. Besides image and data generation, cVAE is typically applied to tasks such as image style conversion and text-to-image generation.

The basic steps of cVAE architecture and algorithm are as follows

• Encoder:

As in normal VAE, the encoder maps the input data to the latent space. However, cVAE also provides additional conditional information to the encoder, which learns a conditional representation of the latent space.

• Latent Space Sampling:

The conditional mean and variance (parameters of the latent space) obtained by the encoder are used to sample from the latent space. This yields the latent variables according to the conditions.

• Decoder:

With the latent variables and condition information as input, the decoder is responsible for reconstructing the original data. Decoders are usually trained to ensure that the generated data is conditionally correct.

• Loss function:

The training of cVAE is done by a loss function based on the reconstruction error and the regularization term in the latent space. Typically, the reconstruction error represents the error between the input data and the output of the decoder, and the regularization term in the latent space is the term to keep the latent variables close. To incorporate conditional information, a term is also added to the loss function to evaluate the similarity between the conditional information and the generated data.

<Algorithm>

The conditional VAE algorithm is a generative model of a normal variational autoencoder with additional conditional information. The basic steps of the conditional VAE algorithm are shown below.

• Encoder:

Takes input data (e.g., images) and conditional information (e.g., class labels and text descriptions) as input and outputs the mean and variance of the latent space. This yields the probability distribution of the latent variable.

• Sampling the latent space:

The mean and variance obtained from the encoder are used to sample the latent variable from a Gaussian distribution. This latent variable represents the conditional latent space representation.

• Decoder:

A decoder is used to reconstruct the original data using the sampled latent variable and conditional information as input. The decoder is typically trained to ensure that the generated data is conditionally correct.

• Loss Function:

The loss function of cVAE consists of the following three terms

• • Reconstruction error: a term to minimize the error between the input data and the output of the decoder.
  • Latent space regularization term: a KL divergence term to bring the latent variables closer to a normal distribution.
  • Conditional Generation Error: A term to evaluate the error between the generated data and the conditional information. This term serves to control the generated data based on the conditions.
• Training:

Trains the encoder and decoder parameters. The model is updated using the reconstruction error, the regularization term in the latent space, and the conditional generating error so as to minimize the loss function.

The conditional VAE algorithm is extended by incorporating conditional information into the regular VAE algorithm. As a generative model, cVAE, with its ability to generate data based on conditions, is applied to tasks such as image generation, image style transformation, and text-to-image generation.

<Implementation>

A simple example of conditional VAE (cVAE) implementation is shown below. The following is a Python code example using TensorFlow. In an actual project, detailed adjustments and hyperparameters will be necessary.

First, import the necessary libraries.

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras.losses import mse

Next, define the encoder and decoder for the conditional VAE.

def build_encoder(input_dim, condition_dim, latent_dim):
    input_layer = Input(shape=(input_dim,))
    condition_layer = Input(shape=(condition_dim,))
    combined_inputs = tf.keras.layers.concatenate([input_layer, condition_layer])

    x = Dense(256, activation='relu')(combined_inputs)
    x = Dense(128, activation='relu')(x)
    mean = Dense(latent_dim)(x)
    log_var = Dense(latent_dim)(x)

    return Model(inputs=[input_layer, condition_layer], outputs=[mean, log_var])

def build_decoder(input_dim, condition_dim, latent_dim):
    input_layer = Input(shape=(input_dim,))
    condition_layer = Input(shape=(condition_dim,))
    latent_layer = Input(shape=(latent_dim,))
    combined_inputs = tf.keras.layers.concatenate([input_layer, condition_layer, latent_layer])

    x = Dense(128, activation='relu')(combined_inputs)
    x = Dense(256, activation='relu')(x)
    output_layer = Dense(input_dim, activation='sigmoid')(x)

    return Model(inputs=[input_layer, condition_layer, latent_layer], outputs=output_layer)

Once the encoder and decoder models are defined, the cVAE assembly and training loops are defined.

input_dim = ...  # Number of input dimensions
condition_dim = ...  # Conditional dimension number
latent_dim = ...  # Number of dimensions of latent variable

encoder = build_encoder(input_dim, condition_dim, latent_dim)
decoder = build_decoder(input_dim, condition_dim, latent_dim)

# Sampling function for latent variables
def sampling(args):
    mean, log_var = args
    epsilon = tf.keras.backend.random_normal(shape=(tf.shape(mean)[0], latent_dim), mean=0., stddev=1.0)
    return mean + tf.exp(log_var / 2) * epsilon

latent_input = Input(shape=(latent_dim,))
sampled_latent = Lambda(sampling)([mean, log_var])
decoded_output = decoder([input_layer, condition_layer, sampled_latent])

cVAE = Model(inputs=[input_layer, condition_layer], outputs=decoded_output)

# loss function
reconstruction_loss = mse(input_layer, decoded_output)
kl_loss = -0.5 * tf.reduce_mean(1 + log_var - tf.square(mean) - tf.exp(log_var))
total_loss = reconstruction_loss + kl_loss

cVAE.add_loss(total_loss)

# training loop
epochs = ...
batch_size = ...

for epoch in range(epochs):
    for _ in range(steps_per_epoch):
        batch_input_data, batch_conditions = ...  # Retrieve batch input data and conditions
        cVAE.train_on_batch([batch_input_data, batch_conditions])

Reference Information and Reference Books

Detailed information on deep learning is provided in the “Deep Learning” section. For more detailed information on deep learning, please refer to “About Deep Learning. For generators, see “Codeless Generation Module Using text-generation-webui and AUTOMATIC1111” “Overview of Automatic Sentence Generation Using Huggingface” “On Attention in Deep Learning,” “Python and Keras for Generative Deep Learning (1)” and “Evolutionary Deep Learning with PyTorch“.

Reference book is “Generative Deep Learning“

“Deep Learning for Coders with fastai and PyTorch“

“Deep Learning with R“

“Deep Reinforcement Learning with Python“