NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

Last update: Dec 23, 2022

Overview

[Official] FINE Samples for Learning with Noisy Labels

This repository is the official implementation of "FINE Samples for Learning with Noisy Labels" paper presented in NeurIPS 2021. New version of previous repository https://github.com/jaychoi12/FINE. Future code modifications and official developments will take place here. Thanks to the contributors in the previous repo.

Reference Codes

We refer to some official implementation codes

Requirements

This codebase is written for python3 (used python 3.7.6 while implementing).
To install necessary python packages, run pip install -r requirements.txt.

Training

Sample-Selection Approaches and Collaboration with Noise-Robust loss functions

Most code strucutres are similar with the original implementation code in https://github.com/bhanML/Co-teaching and https://github.com/shengliu66/ELR.
If you want to train the model with FINE, move to the folder dynamic_selection and run the bash files by following the README.md.

Semi-Supervised Approaches

Most codes are similar with the original implementation code in https://github.com/LiJunnan1992/DivideMix.
If you want to train the model with FINE (f-dividemix), move to the folder dividemix and run the bash files by following the README.md in the dividemix folder.

Results

You can reproduce all results in the paper with our code. All results have been described in our paper including Appendix. The results of our experiments are so numerous that it is difficult to post everything here. However, if you experiment several times by modifying the hyperparameter value in the .sh file, you will be able to reproduce all of our analysis.

Contact

Jongwoo Ko : [email protected]
Taehyeon Kim : [email protected]

License
This project is licensed under the terms of the MIT license.

Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) [No.2019-0-00075, Artificial Intelligence Graduate School Program (KAIST)] and [No. 2021-0-00907, Development of Adaptive and Lightweight Edge-Collaborative Analysis Technology for Enabling Proactively Immediate Response and Rapid Learning].

You might also like...

Official implementation of NeurIPS 2021 paper "Contextual Similarity Aggregation with Self-attention for Visual Re-ranking"

CSA: Contextual Similarity Aggregation with Self-attention for Visual Re-ranking PyTorch training code for CSA (Contextual Similarity Aggregation). We

19 Oct 21, 2022

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (NeurIPS 2021) Code for our NeurIPS 2021 paper 'Exploiting the Intri

53 Dec 25, 2022

Boosted CVaR Classification (NeurIPS 2021)

Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar NeurIPS 2021 Table of Contents Quick Start Train

4 Feb 15, 2022

Official code for On Path Integration of Grid Cells: Group Representation and Isotropic Scaling (NeurIPS 2021)

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling This repo contains the official implementation for the paper On Path Int

39 Nov 10, 2022

This GitHub repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

About Repository This repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.' About Code

1 Nov 9, 2021

Pytorch implementation of RED-SDS (NeurIPS 2021).

Recurrent Explicit Duration Switching Dynamical Systems (RED-SDS) This repository contains a reference implementation of RED-SDS, a non-linear state s

10 Dec 2, 2022

Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

17 Jan 1, 2023

[NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training

Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Code for NeurIPS 2021 paper "Better Safe Than Sorry: Preventing Delu

29 Sep 20, 2022

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Directed Graph Contrastive Learning The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL). In this paper, we present the first con

28 Jan 8, 2023

Comments

Motivation of the method

Hello,

I have read your paper and find it very interesting. However, I may have some confusion about your method. If I understood correctly, the first eigenvector represents the latent distribution of a class, which is similar as the function of a prototype. And I also saw some methods utilize the similarity between a sample and the class-prototype to select clean samples. I would like to know what is the advantage of using the eigenvector over prototypes.

Thanks.

opened by hathawayxxh 5
tabular data/ noisy instances/ new datasets
Hi, thanks for sharing your implementation. I have some questions about it:

Does it also work on tabular data?

Is the code tailored to the datasets used in the paper or can one apply it to any data?

Is it possible to identify the noisy instances (return the noisy IDs or the clean set)?

Thanks!
opened by nazaretl 2
non-exist functions

Hi, thanks for sharing your implementation. In your implementation, threr are some code error. The functions ,get_out_list and get_singular_value_vector, used in FINE_official/dynamic_selection/traintools/gtrobustlosstrain.py is not really so non-existent. Can you give me details about these functions?

opened by OshitaTakumi 0
Could you provide hyperparameters?

I found some hyperparameters are different from the description in the paper, "All hyper-parameters settings are the same with [25], even for the clean probability threshold.". For example, the threshold in the code is 0.6, but in DivideMix the threshold is 0.5. The number of warmup is also different from DivideMix. Could you provide hyperparameters for reproducing the results?

opened by bybeye 0

Owner

mythbuster

GitHub

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax

[NeurIPS 2021] Galerkin Transformer: linear attention without softmax Summary A non-numerical analyst oriented explanation on Toward Data Science abou

159 Dec 20, 2022

Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

CDN Code for our NeurIPS 2021 paper "Mining the Benefits of Two-stage and One-stage HOI Detection". Contributed by Aixi Zhang*, Yue Liao*, Si Liu, Mia

71 Dec 14, 2022

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

81 Dec 16, 2022

Code to reproduce the experiments from our NeurIPS 2021 paper " The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective"

Code To run: python runner.py new --save <SAVE_NAME> --data <PATH_TO_DATA_DIR> --dataset <DATASET> --model <model_name> [options] --n 1000 - train - t

5 Dec 12, 2022

Companion code for the paper "An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence" (NeurIPS 2021)

ReLU-GP Residual (RGPR) This repository contains code for reproducing the following NeurIPS 2021 paper: @inproceedings{kristiadi2021infinite, title=

4 Dec 26, 2021