Image Compression and Generation using Variational Autoencoders in Python

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In this Guided Project, you will:

How to preprocess and prepare data for vision tasks using PyTorch

What a variational autoencoder is and how to train one

How to compress, reconstruct, and generate new images using a generative model

Clock90 minutes
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project, you will be introduced to the Variational Autoencoder. We will discuss some basic theory behind this model, and move on to creating a machine learning project based on this architecture. Our data comprises 60.000 characters from a dataset of fonts. We will train a variational autoencoder that will be capable of compressing this character font data from 2500 dimensions down to 32 dimensions. This same model will be able to then reconstruct its original input with high fidelity. The true advantage of the variational autoencoder is its ability to create new outputs that come from distributions that closely follow its training data: we can output characters in brand new fonts. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Image CompressionMachine LearningVision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. An introduction to the variational autoencoder and our project

  2. Dataset visualization and preprocessing

  3. Dataset split into training and validation sets

  4. U​se data loaders to handle memory overload

  5. Create VAE architecture

  6. Create training loop for VAE

  7. R​esults of our model and short introduction to other potential projects using a VAE

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

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