/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

 yang-song.net
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song
| | jxmo.io
Introduction to variational autoencoders - Jack Morris
1.2 parsecs away

Travel
| | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents.
| | lilianweng.github.io
What are Diffusion Models? | Lil'Log
1.3 parsecs away

Travel
| | [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section.
| | github.com
GitHub - yang-song/score_sde: Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) ยท GitHub
0.7 parsecs away

Travel
| | Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral) - yang-song/score_sde
| | blog.keras.io
Building Autoencoders in Keras
13.3 parsecs away

Travel
| [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics.