Image Generation¶
Variational Autoencoder (VAE)¶
Autoencoder Variational Autoencoder Image Generation
A Variational Autoencoder (VAE) is a generative model that learns to encode input data into a probabilistic latent space and reconstruct it through a decoder. Unlike traditional autoencoders, VAEs model uncertainty by learning a distribution over latent variables, enabling smooth sampling and interpolation.
D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” International Conference on Learning Representations (ICLR), 2014.
Name |
Model |
Input Shape |
|---|---|---|
VAE |
\((N,C,H,W)\) |
DDPM¶
Diffusion Image Generation
A Denoising Diffusion Probabilistic Model (DDPM) is a generative model that learns to generate data by reversing a gradual noising process. It adds Gaussian noise over several timesteps and trains a neural network to denoise and recover the original data distribution through a Markovian process.
J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” Advances in Neural Information Processing Systems (NeurIPS), 2020.
Name |
Model |
Input Shape |
Parameter Count |
|---|---|---|---|
DDPM |
\((N,C,H,W)\) |
20,907,649 (Default) |
To be implemented…🔮