diffusion generative model

A simple tutorial of Variational AutoEncoders with Pytorch

PyTorch denoising diffusion demo

Awesome Best Papers in Computer Vision Top Conference(CVPR/ICCV/ECCV)

A curated list of the most impressive AI papers

A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation.

Sandbox for training deep learning networks

Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Spectral embedding using Laplacian Eigenmaps

CVPR 2024 论文和开源项目合集

2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，思想类，数学类，人物传记书籍

深度学习经典、新论文逐段精读

FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)

A high-quality ellipse detector based on arc-support line segments which can both accurately and efficiently detect ellipses in images.

This is the implementation in C++ of the paper: 'A fast and effective ellipse detector for embedded vision applications'

Joint deep network for feature line detection and description

Official implementation of paper "TP-LSD: Tri-points based line segment detector" .

Holistically-Attracted Wireframe Parsing [TPAMI'23] & [CVPR' 20]

a Line Segment Detector

Implementation of the paper "DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients"

Implement code of paper "ElDet: Anchor-free General Ellipse Object Detector"

ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

Code for CRATE (Coding RAte reduction TransformEr).

Exercises answers to the book "machine-learning" written by Zhou Zhihua。周志华《机器学习》课后习题，个人解答。各算法都拿numpy和pandas实现了一遍

Collection of reproducible deep learning for compressive sensing

A playbook for systematically maximizing the performance of deep learning models.

Awesome work on object 6 DoF pose estimation

Resources (papers, datasets, rendering methods) in the domain of object pose estimation.

A practical approach to learning machine learning.