Machine learning, in numpy
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Updated
Oct 29, 2023 - Python
A variational autoencoder (VAE) is a generative model that combines deep learning with Bayesian inference to learn compact latent representations of data. VAEs are widely used for image generation, anomaly detection, and data augmentation.
Machine learning, in numpy
A Collection of Variational Autoencoders (VAE) in PyTorch.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Collection of generative models in Tensorflow
Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)
Advanced Deep Learning with Keras, published by Packt
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Vector Quantized VAEs - PyTorch Implementation
List of Molecular and Material design using Generative AI and Deep Learning
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Experiments for understanding disentanglement in VAE latent representations
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
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
Pytorch implementation of β-VAE