How to Generate Images - Intro to Deep Learning #14

Hello, World: Exploring Variational Autoencoders with Siraj Raval

Hello, world! It's Siraj, and today we're going to explore a really cool model called a variational autoencoder. What happens when we encounter data with no labels? Can we still learn from it? These are the questions we'll be answering as we delve into the world of unsupervised learning.

Most recent successes in deep learning have been due to our ability to train neural networks on huge sets of cleanly labeled data. If you have a set of inputs and their respective target labels, you can try and learn the probability of a particular label for a particular input. Intuitively, this makes sense. We're just learning a mapping of values. But the hottest area of research right now is learning from raw, unlabeled data that the world is absolutely brimming with. Unsupervised learning.

One type of model that can do this is called an autoencoder. Autoencoders sequentially de-construct input data into hidden representations, then use those same representations to sequentially reconstruct outputs that resemble their originals. Autoencoding is considered a data compression algorithm, but they're not perfect and can distort the original data in certain ways.

The concept of unsupervised learning may seem daunting at first, but it's actually quite straightforward once you understand the basics. Unsupervised learning involves training a machine learning model without any labeled data. The goal is to find patterns or relationships within the unlabeled data that can be useful for making predictions or generating new data.

One way to approach unsupervised learning is through the use of autoencoders. An autoencoder is a type of neural network that consists of two parts: an encoder and a decoder. The encoder takes in a input, processes it using a series of transformations, and produces a compressed representation of the input. The decoder then takes this compressed representation and expands it back into its original form.

Variational Autoencoders (VAEs) are a type of autoencoder that adds a new layer of complexity to the traditional autoencoder design. In addition to producing a compressed representation of the input, VAEs also produce a probability distribution over the input space. This distribution is known as the latent space, and it represents all possible versions of the input data.

The key advantage of VAEs is that they can be used for both dimensionality reduction and generative modeling. In dimensionality reduction, the goal is to reduce the number of dimensions in a dataset while preserving its essential characteristics. In generative modeling, the goal is to generate new data that is similar to the existing data.

So, how do VAEs work? The process begins with training the encoder network. This involves feeding the input data into the encoder and adjusting the weights and biases of the layers until the output matches a pre-defined target distribution. Once the encoder is trained, we can use it to generate new data by sampling from the latent space.

The next part of our journey will delve deeper into how VAEs work by examining the components that make them up.