Let's make a conclusion about considered autoencoder models:

<!DOCTYPE html>
<html>
<head>
    <style>
        table {
            width: 80%;
            margin: 20px auto;
            border-collapse: collapse;
            border: 1px solid #ddd;
        }
        th,
        td {
            padding: 10px;
            text-align: left;
        }
        th {
            background-color: #f2f2f2;
        }
        tr:nth-child(even) {
            background-color: #f2f2f2;
        }
        tr:hover {
            background-color: #ddd;
        }
        th strong {
            font-weight: bold;
        }
        td strong {
            font-weight: bold;
        }
    </style>
</head>
<body>

<h2>Comparison of Simple Autoencoder, Variational Autoencoder (VAE), and Conditional Variational Autoencoder (CVAE)</h2>

<table>
    <tr>
        <th><strong>Aspect</strong></th>
        <th>Simple Autoencoder</th>
        <th>Variational Autoencoder (VAE)</th>
        <th>Conditional Variational Autoencoder (CVAE)</th>
    </tr>
    <tr>
        <td><strong>Objective</strong></td>
        <td>Learn to compress and reconstruct data.</td>
        <td>Generate new data samples similar to training data using a probabilistic approach.</td>
        <td>Generate new data samples with controlled attributes through conditional inputs.</td>
    </tr>
    <tr>
        <td><strong>Architecture</strong></td>
        <td>Encoder and Decoder without probabilistic elements.</td>
        <td>Encoder, Latent Space (mean and variance), Decoder.</td>
        <td>Encoder, Latent Space (mean and variance) with additional conditional input, Decoder.</td>
    </tr>
    <tr>
        <td><strong>Latent Space</strong></td>
        <td>Deterministic.</td>
        <td>Probabilistic (learns a distribution).</td>
        <td>Probabilistic (learns a distribution) with conditional input.</td>
    </tr>
    <tr>
        <td><strong>Training Loss</strong></td>
        <td>Reconstruction loss (e.g., Mean Squared Error).</td>
        <td>Reconstruction loss + KL divergence loss.</td>
        <td>Reconstruction loss + KL divergence loss with conditional input.</td>
    </tr>
    <tr>
        <td><strong>Sampling from Latent Space</strong></td>
        <td>Not applicable (deterministic).</td>
        <td>Sample from learned distribution (e.g., Gaussian).</td>
        <td>Sample from learned distribution with conditional input.</td>
    </tr>
    <tr>
        <td><strong>Applications</strong></td>
        <td>Dimensionality reduction, denoising.</td>
        <td>Data generation, anomaly detection.</td>
        <td>Controlled data generation, attribute manipulation.</td>
    </tr>
    <tr>
        <td><strong>Advantages</strong></td>
        <td>Simplicity, efficient for basic reconstruction tasks.</td>
        <td>Generates diverse outputs, captures data distribution.</td>
        <td>Provides explicit control over generated outputs based on conditions.</td>
    </tr>
    <tr>
        <td><strong>Disadvantages</strong></td>
        <td>Not suitable for generating new data; lacks probabilistic nature.</td>
        <td>Difficulty in balancing reconstruction and latent space regularization.</td>
        <td>Complexity in conditional input integration; requires labeled data.</td>
    </tr>
</table>

</body>
</html>


Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

In this section, we'll start by understanding different types of generative models. We'll look into autoencoders, which transform data, GANs, which use a competitive approach, and diffusion networks, which spread out data. You'll learn how each model generates data differently, paving the way for further exploration in generative networks.

We will implement a simple Variational Autoencoder (VAE) using the MNIST dataset. We will build and train the VAE model, which includes designing the encoder and decoder networks, sampling from the latent space using the reparameterization trick, and optimizing the combined reconstruction and Kullback-Leibler divergence loss.

Now we will explore the generator-discriminator principle fundamental to GANs, implement a simple GAN model, and examine various GAN variations. Additionally, we will provide an overview of some existing state-of-the-art image generation models.

Image Synthesis Through Generative Networks

Models Comparison