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K-Means clustering - constrained with minimum and maximum cluster size. Documentation: https://joshlk.github.io/k-means-constrained
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
📈 目前最大的工业缺陷检测数据库及论文集 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance.
Anomaly detection related books, papers, videos, and toolboxes
ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data.
PyTorch implementation of consistency regularization methods for semi-supervised learning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Project repository for Secrets of Matrix Factorization (ICCV 2015)
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face D…
The implementation of Split Multiplicative Multi-view Subspace Clustering, to appear in T-IP
A curated list of resources for Learning with Noisy Labels
This introduces a suggestion of mathematical notation protocol for machine learning.
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
A pytorch implementation of SSH face detector (Single Shot Headless face detector)
A Light CNN for Deep Face Representation with Noisy Labels, TIFS 2018
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
😮python模拟登陆一些大型网站，还有一些简单的爬虫，希望对你们有所帮助❤️，如果喜欢记得给个star哦🌟
A clean, simple and extensible template for presentations. Supports XeTeX and Beamer.
A minimalist and modern Beamer theme
PyTorch implementations of Generative Adversarial Networks.
Graph Neural Network Library for PyTorch
A curated list of community detection research papers with implementations.
💬 A better WeChat on macOS and Linux. Built with Electron by Zhongyi Tong.