noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

Last update: Dec 04, 2022

Overview

ProSelfLC: CVPR 2021

ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks

For any specific discussion or potential future collaboration, please feel free to contact me.

Paper link: https://arxiv.org/abs/2005.03788

Cite our work if you find it useful

@inproceddings{wang2021proselflc,
  title={ {ProSelfLC}: Progressive Self Label Correction
  for Training Robust Deep Neural Networks},
  author={Wang, Xinshao and Hua, Yang and Kodirov, Elyor and Clifton, David A and Robertson, Neil M},
  booktitle={CVPR},
  year={2021}
}

Link to Slide, Poster, Final version

Link to reviewers' comments

List of Content

A GOOD REPRESENTATION DETECTS NOISY LABELS

A GOOD REPRESENTATION DETECTS NOISY LABELS This code is a PyTorch implementation of the paper: Prerequisites Python 3.6.9 PyTorch 1.7.1 Torchvision 0.

64 Jan 4, 2023

A Light CNN for Deep Face Representation with Noisy Labels

A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=

715 Nov 5, 2022

Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

43 Dec 26, 2022

[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

268 Dec 24, 2022

The repo of the preprinting paper "Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection"

Inferring Spatial Uncertainty in Object Detection A teaser version of the code for the paper Labels Are Not Perfect: Inferring Spatial Uncertainty in

21 Mar 3, 2022

Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

4 Jul 12, 2021

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

2 Dec 17, 2021

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

49 Jan 2, 2023

Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness

Orthogonalizing Convolutional Layers with the Cayley Transform This repository contains implementations and source code to reproduce experiments for t

36 Dec 30, 2022

Comments

Full code for reproducing the resutls

Thanks for your excellent work. May I ask whether the full code will be released for reproducing the results and developing new methods based on your work?

Regards

Lei

opened by LeiBAI 4
tabular data/ noisy instances
Hi, thanks for sharing your implementation. I have two questions about it:

Does it also work on tabular data?

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

Thanks!
opened by nazaretl 1
Further research: Not All Knowledge Is Created Equal https://arxiv.org/abs/2106.01489

Not All Knowledge Is Created Equal Ziyun Li, Xinshao Wang, Haojin Yang, Di Hu, Neil M. Robertson, David A. Clifton, Christoph Meinel

arXiv: https://arxiv.org/abs/2106.01489

Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, not all knowledge is certain and correct, especially under adverse conditions. For example, label noise usually leads to less reliable models due to the undesired memorisation [1, 2]. Wrong knowledge misleads the learning rather than helps. This problem can be handled by two aspects: (i) improving the reliability of a model where the knowledge is from (i.e., knowledge source's reliability); (ii) selecting reliable knowledge for distillation. In the literature, making a model more reliable is widely studied while selective MKD receives little attention. Therefore, we focus on studying selective MKD and highlight its importance in this work.

Concretely, a generic MKD framework, Confident knowledge selection followed by Mutual Distillation (CMD), is designed. The key component of CMD is a generic knowledge selection formulation, making the selection threshold either static (CMD-S) or progressive (CMD-P). Additionally, CMD covers two special cases: zero knowledge and all knowledge, leading to a unified MKD framework. We empirically find CMD-P performs better than CMD-S. The main reason is that a model's knowledge upgrades and becomes confident as the training progresses.

Extensive experiments are present to demonstrate the effectiveness of CMD and thoroughly justify the design of CMD. For example, CMD-P obtains new state-of-the-art results in robustness against label noise.

opened by XinshaoAmosWang 0

Releases(v2.0.0)

v2.0.0(Oct 20, 2021)

Source code(tar.gz)
Source code(zip)
v0.1(Mar 18, 2021)

The studied label correction methods are implemented as loss functions, being easy to use and can replace exisiting loss functions.
Source code(tar.gz)
Source code(zip)

Owner

amos_xwang

Deep Metric Learning, Robust Deep Learning, Semisupervised Learning, Label Noise, Sample Imbalance...

GitHub Repository

Predicts an answer in yes or no.

Oui-ou-non-prediction Predicts an answer in 'yes' or 'no'. It is based on the game 'effeuiller la marguerite' in which the person plucks flower petals

1 Jan 15, 2022

Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

[Paper] [Project page] This repository contains code for the paper: Andrew Owens, Alexei A. Efros. Audio-Visual Scene Analysis with Self-Supervised Mu

202 Dec 13, 2022

code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022

Deep-learning-roadmap - All You Need to Know About Deep Learning - A kick-starter

Deep Learning - All You Need to Know Sponsorship To support maintaining and upgrading this project, please kindly consider Sponsoring the project deve

4.4k Dec 26, 2022

BlockUnexpectedPackets - Preventing BungeeCord CPU overload due to Layer 7 DDoS attacks by scanning BungeeCord's logs

BlockUnexpectedPackets This script automatically blocks DDoS attacks that are sp

3 Mar 31, 2022

A Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Training Data》

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

7 Nov 27, 2021

A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

Transcription Factor binding predictions with Attention and Transformers A repository with exploration into using transformers to predict DNA ↔ transc

62 Dec 20, 2022

Semantic segmentation models, datasets and losses implemented in PyTorch.

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

1.3k Jan 07, 2023

Feup-csr - Repository holding my group's submission to the CSR project competition

CSR Competições de Swarm Robotics Swarm Robotics Competitions This repository holds the files submitted for the CSR project competition. Project group

1 Jan 04, 2022

SwinIR: Image Restoration Using Swin Transformer

SwinIR: Image Restoration Using Swin Transformer This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Win

2.4k Jan 05, 2023

Lightweight Face Image Quality Assessment

LightQNet This is a demo code of training and testing [LightQNet] using Tensorflow. Uncertainty Losses: IDQ loss PCNet loss Uncertainty Networks: Mobi

5 Nov 18, 2022

Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022

上海交通大学全自动抢课脚本，支持准点开抢与抢课后持续捡漏两种模式。2021/06/08更新。

Welcome to Course-Bullying-in-SJTU-v3.1！ 2021/6/8 紧急更新v3.1 更新说明为了更好地保护用户隐私，将原来用户名+密码的登录方式改为微信扫二维码+cookie登录方式，不再需要配置使用pytesseract。在使用扫码登录模式时，请稍等，二维码将马

87 Sep 13, 2022

This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022

This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

PyTorch implementation of DAQ This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021. For more informatio

36 Nov 04, 2022

Few-shot Learning of GPT-3

Few-shot Learning With Language Models This is a codebase to perform few-shot "in-context" learning using language models similar to the GPT-3 paper.

224 Dec 28, 2022

Torchlight2 lan game server tool - A message forwarding tool for Torchlight 2 lan game

Torchlight 2 Lan Game Server Tool A message forwarding tool for Torchlight 2 lan

3 Nov 01, 2022

U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

6.5k Jan 09, 2023

Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing

Notice: Support for Python 3.6 will be dropped in v.0.2.1, please plan accordingly! Efficient and Scalable Physics-Informed Deep Learning Collocation-

74 Dec 09, 2022

MDETR: Modulated Detection for End-to-End Multi-Modal Understanding

MDETR: Modulated Detection for End-to-End Multi-Modal Understanding Website • Colab • Paper This repository contains code and links to pre-trained mod

770 Dec 28, 2022