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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structured-generative-modeling

Related tags

Overview

Main Requirements

Usage

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

Each file can be run independently

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

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

Owner

render sprites into your desktop environment as shaped windows using GTK

BigbrotherBENL - Face recognition on the Big Brother episodes in Belgium and the Netherlands.

[ICCV 2021] Deep Hough Voting for Robust Global Registration

An official implementation of "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation" (CVPR 2021) in PyTorch.

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Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

Improving Object Detection by Label Assignment Distillation

Code of paper "Compositionally Generalizable 3D Structure Prediction"

A numpy-based implementation of RANSAC for fundamental matrix and homography estimation. The degeneracy updating and local optimization components are included and optional.

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