Lecture

Deep Generative Models: Variational Autoencoders & GANs

Description

This lecture covers deep generative models, focusing on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). VAEs aim to learn latent representations and generate new data by sampling from the learned distribution. GANs consist of a generator and a discriminator, where the generator aims to produce realistic samples to fool the discriminator. The lecture discusses the training challenges, weaknesses, and potential solutions for GANs, such as Wasserstein GANs. It also explores other generative models like DALL-E, which can create images from text descriptions. The session concludes with a demonstration of various generative models and their applications.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.