Popular GitHub Repositories for Factor Vae

Machine learning, in numpy

Code for the paper "Jukebox: A Generative Model for Music"

A Collection of Variational Autoencoders (VAE) in PyTorch.

Tiled Diffusion and VAE optimize, licensed under CC BY-NC-SA 4.0

Collection of generative models in Tensorflow

Advanced Deep Learning with Keras, published by Packt

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

Upscale an image by a factor of 4, while generating photo-realistic details.

Vector Quantized VAEs - PyTorch Implementation

List of Molecular and Material design using Generative AI and Deep Learning

Experiments for understanding disentanglement in VAE latent representations

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

(CVPR 2023) Pytorch implementation of “T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations”

A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).

PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349

To learn and reason like humans, AI must first learn to factorise interpretable representations of independent data generative factors (preferably in an unsupervised manner!!). What does all this mean? Go through this tutorial to get an overview of disentanglement in the context of unsupervised visual disentangled representation learning.

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Tensorflow implementation of variational auto-encoder for MNIST