TopoFilter
by pxiangwu
NeurIPS 2020, "A Topological Filter for Learning with Label Noise".
AI summary
Label noise mitigation
Develops and evaluates machine learning algorithms to mitigate the effects of noisy labels in supervised learning.
- stars
- 30
- forks
- 7
- watching
- 3
Similar projects
Found by comparing what the projects do, not just their names.
Label noise mitigation method
An implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks
Label noise mitigator
An implementation of a method to improve model robustness against inherent label noise in machine learning models
Noisy label solver
A PyTorch implementation of a method for learning with noisy labels in deep neural networks
Sample selection
Implementation of a method to improve machine learning models trained with noisy labels by selecting and collaborating with high-quality samples
Label correction library
Provides PyTorch implementation of a method to address noisy labels in medical image segmentation.
Label noise toolset
Provides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels
Label noise correction algorithm
An implementation of an unsupervised label noise modeling and loss correction approach for deep learning.
Noisy label learning
A curated collection of papers and resources on learning with noisy labels in machine learning
Gradient clipping mitigation
An implementation of gradient clipping as a method to mitigate the effects of noisy labels in machine learning models
Label noise correction
A method to train deep learning classifiers on noisy labels using a small set of trusted data
Label noise trainer
An implementation of a method to learn with instance-dependent label noise in deep learning models using PyTorch
Label smoothing algorithm
An implementation of a method to learn from noisy labels in machine learning models with instance-dependent noise
Label correction model
Tackles label noise in machine learning by developing a probabilistic model to correct instance-dependent errors
Label noise study
An investigation into deep learning models trained with noisy labels and methods to improve their accuracy.
Label correction algorithm
An algorithm designed to robustly correct noisy labels in training data by iteratively refining the network's confidence and updating the loss function.