Variational Autoencoders (VAEs)
Variational Autoencoders also known as VAEs are one of the most powerful generative models used for Image Generation. Likewise, GANs also use two neural networks named encoder and decoder but, in a bit, different manner.
VAEs have two important components:
- The Encoder is usually a CNN (Convolutional Neural Network) that reduces the input image to a latent space representation (mean & variance vectors).
- The Decoder is also a CNN (Convolutional Neural Network) model that takes the latent space representation and performs the task of reconstruction of the image.
VAEs Architecture
In VAEs, the input image is passed through the encoder to get the mean and variance vectors representing latent space. Then a sample point is taken in the latent space by using the reparameterization technique. After that, the sampled point from the latent space is fed into the decoder i.e., a neural network to generate a reconstructed image.
VAEs loss function consists of two parts:
- Reconstruction Loss (It measures the difference between the input image provided to the encoder and the reconstructed image generated by the decoder)
- and KL Divergence Loss (It helps in preventing overfitting by ensuring that the learned latent space is close to a unit Gaussian Distribution).
During training our aim to minimize the combined loss using techniques like Backpropagation. And by updating the weights in Encoder and Decoder. Gradually this process will improve their ability to encode and decode images efficiently.
Also check:
- What is Generative AI?
- Generate Images With OpenAI in Python
- How to Generate AI Images in Google Search With a Text Description
- How To Use DALL.E 2 To Create AI Images?
How does an AI Model generate Images?
We all are living in an era of Artificial Intelligence and have felt its impact. There are numerous AI tools for various purposes ranging from Text Generation to image Generation to Video Generation to many more things. You must have used text-to-image models like Dall-E3, Stable Diffusion, MidJourney, etc. And it might be that you’re fascinated with their image-generation capabilities as they can generate realistic images of non-existent objects or can enhance existing images. They can convert your imagination into an image in a matter of seconds. But how?
In this article, we are going to explore how all these TTM models have this kind of imagination that can generate images that they’ve never seen.
How does an AI generate images?
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