structured-generative-modeling

Last update: Oct 11, 2021

Overview

This repository contains the implementation for the paper Information Theoretic StructuredGenerative Modeling,

Specially thanks for the open-source codes shared by sagelywizard/pytorch-mdn and PyTorch-GAN

Main Requirements

Pytorch
A GPU Machine

Usage

The main experiments in the paper are put in the notebook format.

Each file can be run independently

GAN Example
MINE Example
Density Estimation
Conditonal Estimation

To run ood_visualization.ipynb, please download the pretrained model in the ./model/ folder. (google drive link later)

The other baselines can be run by calling

python MDN.py
python CGAN.py

GMM_VBGMM_CE.py provides codes for producing conditional CE for any mixture models obtained from scipy.

Simply calling

compute_conditionalCE(joint, gm_joint)

in python to obtain the value, where joint should be bs*K and gm_joint is the class obtained from scipy.

Owner

GitHub Repository

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