coteaching_plus
by bhanML
ICML'19: How does Disagreement Help Generalization against Label Corruption?
AI summary
Co-teaching algorithm
This project implements a PyTorch-based co-teaching algorithm to improve generalization against label corruption in machine learning.
- stars
- 21
- forks
- 3
- watching
- 1
Similar projects
Found by comparing what the projects do, not just their names.
Noisy label training method
Develops a robust training method for deep neural networks using noisy labels
Graph algorithm
An implementation of a deep learning algorithm for graph data
Algorithms book
A comprehensive C programming project covering various algorithms and data structures
Graph clustering library
A PyTorch implementation of a clustering algorithm for graph neural networks
Deep learning toolkit
A Python framework for building deep learning models with optimized encoding layers and batch normalization.
Noisy Supervision Method
An implementation of Masking, a method to improve the robustness of deep neural networks under noisy supervision
Node learner
A PyTorch implementation of node representation learning using multiple social contexts
Pseudo labeling
A PyTorch implementation of a method for improving semi-supervised learning in federated settings by adapting pseudo labels to balance classes.
Vision Language Model
Develops a PyTorch implementation of an enhanced vision language model
PyTorch C++ implementation
A C++ implementation of PyTorch tutorials
Machine learning toolkit
An open-source software project providing tools and examples for building machine learning models in Java
Learning framework
A language that facilitates designing machine learning models with flexible configurations
Machine Learning Tutorials
A collection of examples and code snippets teaching machine learning concepts to security professionals through hands-on Python projects
Community detector
An R package implementing a community detection algorithm using graph degree betweenness.
GCN sampler
Implementation of graph convolutional network algorithms with sampling techniques to improve learning speed and efficiency