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vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

py-spy

Sampling profiler for Python programs

xformers

Hackable and optimized Transformers building blocks, supporting a composable construction.

PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.

lightning-hydra-template

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

LightGlue

LightGlue: Local Feature Matching at Light Speed (ICCV 2023)

pyodbc

Python ODBC bridge

Self-Attention-GAN

Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)

TimeSformer

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

dreamerv3

Mastering Diverse Domains through World Models

NVAE

The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)

mpc.pytorch

A fast and differentiable model predictive control (MPC) solver for PyTorch.

MTR

MTR: Motion Transformer with Global Intention Localization and Local Movement Refinement, NeurIPS 2022.

LaPreprint

📝 A nicely formatted LaTeX preprint template

geometric-gnn-dojo

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.

vdvae

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"

LAV

(CVPR 2022) A minimalist, mapless, end-to-end self-driving stack for joint perception, prediction, planning and control.

mile

PyTorch code for the paper "Model-Based Imitation Learning for Urban Driving".

trajdata

A unified interface to many trajectory forecasting datasets.

TwoStageVAE

Efficient-VDVAE

Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

trajax

SGConv

Stabilizing_GANs

Code for the NIPS17 paper "Stabilizing Training of Generative Adversarial Networks through Regularization"

diffstack

Implementation of DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles - a CoRL 2022 paper by Peter Karkus, Boris Ivanovic, Shie Mannor, Marco Pavone.

Frechet-Inception-Distance

CPU/GPU/TPU implementation of the Fréchet Inception Distance

TrafficBots

TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023. Code is now available at https://github.com/zhejz/TrafficBots

ComplexAutoEncoder

Code for the paper: Complex-Valued Autoencoders for Object Discovery

Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-Autoencoder

We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate the performance of the coupled VAE model using the MNIST dataset. Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and the output values are generated by a more compact latent space. We analyze the histograms of probabilities for the input images using the generalized mean metrics, in which increased geometric mean illustrates that the average likelihood of input data is improved. Increases in the -2/3 mean, which is sensitive to outliers, indicates improved robustness. The decisiveness, measured by the arithmetic mean of the likelihoods, is unchanged and -2/3 mean shows that the new model has better robustness.