zsommer

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

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.

pyprobml

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

transferlearning-tutorial

《迁移学习简明手册》LaTex源码

transferlearning

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

Semi-supervised-learning

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

semi-vit

PyTorch implementation of Semi-supervised Vision Transformers

evolutionary-model-merge

Official repository of Evolutionary Optimization of Model Merging Recipes

TorchSemiSeg

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

ml-visuals

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

open-iad

Image anomaly detection benchmark in industrial manufacturing

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)。

LEAP

A general purpose Library for Evolutionary Algorithms in Python.

deap

Distributed Evolutionary Algorithms in Python

GeneticAlgorithmPython

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

geatpy

Evolutionary algorithm toolbox and framework with high performance for Python

cs-self-learning

计算机自学指南

cs229m_notes

SpatialCorrection

Learning to Segment from Noisy Annotations: A Spatial Correction Approach

MTCL

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

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".

Co-teaching

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

mixup-cifar10

mixup: Beyond Empirical Risk Minimization

cleanlab

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

noisy_label_papers

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

awesome-long-tail-learning

U2PL

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

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

Multi-Attention-Network

The semantic segmentation of remote sensing images