learning-to-reweight-examples
Example weighting
Project implementing a method to improve deep learning model robustness by re-weighting examples with noisy labels
Code for paper "Learning to Reweight Examples for Robust Deep Learning"
269 stars
10 watching
52 forks
Language: Python
last commit: almost 6 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| This is an implementation of a meta-learning algorithm to address class imbalance issues in deep learning models with noisy labels. | 284 |
| An implementation of a method to improve classification accuracy on noisy labels by reweighting their importance | 39 |
| A collection of implementations of recent deep learning papers in Python | 1,814 |
| Enables training and evaluation of deep learning models from Apache Parquet datasets in various machine learning frameworks | 1,805 |
| A semi-supervised learning method to improve the accuracy of machine learning models by using noisy teacher models and student models. | 755 |
| A unified interface to run deep learning models from multiple frameworks using C++ and Python. | 937 |
| A PyTorch implementation of a method for learning with noisy labels in deep neural networks | 97 |
| Develops an instance segmentation and panoptic segmentation model for computer vision tasks. | 648 |
| A collection of resources and examples around machine learning for education and development | 954 |
| A benchmarking suite for multimodal in-context learning models | 31 |
| This project implements a deep metric learning framework using an adversarial auxiliary loss to improve robustness. | 39 |
| Adapting a single network to multiple tasks by learning to mask weights | 183 |
| This repository implements methods to find influential training samples in Gradient Boosted Decision Trees ensembles | 67 |
| An implementation of a plug-and-play language model that allows users to steer the topic and attributes of large language models. | 1,132 |
| An implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks | 75 |