zsommer

cs-self-learning

计算机自学指南

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

awesome-english-ebooks

经济学人(含音频)、纽约客、卫报、连线、大西洋月刊等英语杂志免费下载,支持epub、mobi、pdf格式, 每周更新

transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

cleanlab

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

pyprobml

Python code for "Probabilistic Machine learning" book by Kevin Murphy

deap

Distributed Evolutionary Algorithms in Python

transferlearning-tutorial

《迁移学习简明手册》LaTex源码

geatpy

Evolutionary algorithm toolbox and framework with high performance for Python

GeneticAlgorithmPython

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

awesome-industrial-anomaly-detection

Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。

Semi-supervised-learning

A Unified Semi-Supervised Learning Codebase (NeurIPS'22)

evolutionary-model-merge

Official repository of Evolutionary Optimization of Model Merging Recipes

mixup-cifar10

mixup: Beyond Empirical Risk Minimization

FixMatch-pytorch

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

DivideMix

Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning

TorchSemiSeg

[CVPR 2021] CPS: Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Co-teaching

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

awesome-long-tail-learning

U2PL

[CVPR'22] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

cs229m_notes

open-iad

Image anomaly detection benchmark in industrial manufacturing

LEAP

A general purpose Library for Evolutionary Algorithms in Python.

MTCL

[MICCAI21, TMI22] Mean-Teacher-Assisted Confident Learning

Multi-Attention-Network

The semantic segmentation of remote sensing images

semi-vit

PyTorch implementation of Semi-supervised Vision Transformers

SP_guided_Noisy_Label_Seg

This repository contains a PyTorch implementation of the MICCAI 2021 paper "Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation".

noisy_label_papers

This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.

SpatialCorrection

Learning to Segment from Noisy Annotations: A Spatial Correction Approach