Experimenting with the PPGN-h architecture by adding new discriminators to the layers of the encoder
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Updated
Aug 18, 2018 - Python
Experimenting with the PPGN-h architecture by adding new discriminators to the layers of the encoder
implementation of convolutional VAE in pytorch
Music generation with Wasserstein Autoencoders
📦 Ready to use implementations of state-of-the-art generative models in TensorFlow 2
Re-implemntation of scVI (a deep generative model) using PyTorch, PyTorch Lightning, and Pyro
📦 Ready to use implementations of state-of-the-art generative models in PyTorch
Undersmoothing Causal Estimators with Generative Trees
Generic PyTorch Pipeline for solving Inverse Problems using Score-based Generative Models
Thesis projects
Implementing a Denoising Diffsuion Probabilistic Model (DDPM) on Tensorflow from scratch for Pokémon sprites synthesis
Experimental framework for GAN/VAE research
CFG-GAN: Composite functional gradient learning of generative adversarial models
Using GPT3 to make a horoscope predictor.
Implement and test different types of generative models.
An AI driven Video manipulation toolkit
Denoising Autoencoder (DAE) in PyTorch generalized to a generative model.
Pytorch implementation of Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE paper
On explainable attention-based deep neural networks trained on radiographic data augmented with diffusion models
Experiments in NN diffusion models
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