Linear Variational State Space Filters

Last update: Dec 13, 2021

Overview

Linear Variational State Space Filters

To set up the environment, use the provided scripts in the docker/ folder to build and run the codebase inside a docker container.

Use

./docker/build.sh

to build the container and

./docker/run.sh

to launch a docker container.

Then run lsvae/train_lsvae.py to start training.

Owner

GitHub Repository

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Linear Variational State Space Filters

Related tags

Overview

Linear Variational State Space Filters

Owner

Real-time 3D multi-person detection made easy with OpenPose and the ZED

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Using image super resolution models with vapoursynth and speeding them up with TensorRT

AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis.

Implement of homography net by pytorch

Introducing neural networks to predict stock prices

Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

Code for "Training Neural Networks with Fixed Sparse Masks" (NeurIPS 2021).

A simple Python configuration file operator.

Pytorch implementation of few-shot semantic image synthesis

Paper list of log-based anomaly detection

A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo

Official implementation of Densely connected normalizing flows

Multi-Anchor Active Domain Adaptation for Semantic Segmentation (ICCV 2021 Oral)

Server files for UltimateLabeling

PyTorch implementation of VAGAN: Visual Feature Attribution Using Wasserstein GANs

Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

RRxIO - Robust Radar Visual/Thermal Inertial Odometry: Robust and accurate state estimation even in challenging visual conditions.

RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.