Generative Autoregressive, Normalized Flows, VAEs, Score-based models (GANVAS)

Last update: Nov 26, 2022

Overview

GANVAS-models

This is an implementation of various generative models. It contains implementations of the following:

Autoregressive Models: PixelCNN, GatedPixelCNN
Normalized Flows: Glow
VAEs
Score-based models: Denoising Score Matching

The code is ready to train the models using shapes, colored shapes, MNIST, and colored MNIST. Instructions on how to add new datasets can be found in datasets/datasets.py Models can also be easily added, see instructions in main.py

How to use

Create a Neptune account, create a project and use the API token of your project in the API_TOKEN key in the appropriate config file in the configs folder
Run the command python main.py --configs ./configs/model_name/dataset_name.yaml
You can find the config details of each dataset and logging settings in the yaml files
You will find the training results, generated samples, and others on neptune (make sure that log_neptune is set to True in the yaml file)

Owner

MRSAIL (Mini Robotics, Software & AI Lab)

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