7: Generative Visual Computing

Date: 27th November 2024

💡 This week, we'll dive into the exciting world of generative models for computer vision! We'll explore how to create models that can learn the intrinsic features of a dataset and generate new images. We'll focus on building an auto-encoder, a powerful tool for capturing the essence of visual data in a compressed latent space.

A latent space is a lower-dimensional representation that encodes the most important features of the data. By learning this compact representation, generative models can create new images that resemble the original dataset. We'll introduce you to various state-of-the-art models used in industry and research, such as Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs) and Diffussion Models!💡

Also, I want to remind you guys that we are running a DOXA challenge and which you can get prizes for!

•⁠ ⁠⁠1st place will get a Mystery prize 🍫 + AI Society Shirt 👕+ Pen🖊️ •⁠ ⁠⁠2nd place will receive AI Society Shirt 👕 + Pen. 🖊️(NOTE that 1st and 2nd place have to achieve a score greater than 0.8074) •⁠ ⁠⁠⁠The remaining participants will receive UCL AI Society Pens as long as you can get a score above Jeremy (0.6077)

The deadline for submission is Wednesday, December 4th, 5:59PM which is right before our next session on RNNs.

For more information on how to do better in the challenge access the last 10 slides and watch our tutorial recording below.

You can access our slides here: 💻 Tutorial 7 Slides

The recording from this session is available here: 🎤 Tutorial 7 Recording

We did not go through the demonstration notebook during session, but you can access our it here: 📘 Tutorial 7 Notebook