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

Keras-retinanet - Keras implementation of RetinaNet object detection.

Keras RetinaNet Keras implementation of RetinaNet object detection as described in Focal Loss for Dense Object Detection by Tsung-Yi Lin, Priya Goyal,

4.3k Jan 01, 2023

Combining Diverse Feature Priors

Combining Diverse Feature Priors This repository contains code for reproducing the results of our paper. Paper: https://arxiv.org/abs/2110.08220 Blog

5 Nov 12, 2022

PyTorch reimplementation of minimal-hand (CVPR2020)

Minimal Hand Pytorch Unofficial PyTorch reimplementation of minimal-hand (CVPR2020). you can also find in youtube or bilibili bare hand youtube or bil

228 Dec 29, 2022

Perform zero-order Hankel Transform for an 1D array (float or real valued).

perform zero-order Hankel Transform for an 1D array (float or real valued). An discrete form of Parseval theorem is guaranteed. Suit for iterative problems.

1 Jan 17, 2022

Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors.

PairRE Code for paper PairRE: Knowledge Graph Embeddings via Paired Relation Vectors. This implementation of PairRE for Open Graph Benchmak datasets (

65 Dec 19, 2022

DeepVoxels is an object-specific, persistent 3D feature embedding.

DeepVoxels is an object-specific, persistent 3D feature embedding. It is found by globally optimizing over all available 2D observations of

196 Dec 25, 2022

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

189 Nov 22, 2022

A full pipeline AutoML tool for tabular data

HyperGBM Doc | 中文 We Are Hiring！ Dear folks,we are offering challenging opportunities located in Beijing for both professionals and students who are k

240 Jan 03, 2023

Pytorch implementation of "Neural Wireframe Renderer: Learning Wireframe to Image Translations"

Neural Wireframe Renderer: Learning Wireframe to Image Translations Pytorch implementation of ideas from the paper Neural Wireframe Renderer: Learning

7 Nov 14, 2022

Weakly Supervised 3D Object Detection from Point Cloud with Only Image Level Annotation

SCCKTIM Weakly Supervised 3D Object Detection from Point Cloud with Only Image-Level Annotation Our code will be available soon. The class knowledge t

1 Nov 12, 2021

Cortex-compatible model server for Python and TensorFlow

Nucleus model server Nucleus is a model server for TensorFlow and generic Python models. It is compatible with Cortex clusters, Kubernetes clusters, a

14 Nov 27, 2022

SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

24 May 20, 2022

Python Blood Vessel Topology Analysis

Python Blood Vessel Topology Analysis This repository is not being updated anymore. The new version of PyVesTo is called PyVaNe and is available at ht

6 Nov 15, 2022

Language Used: Python . Made in Jupyter(Anaconda) notebook.

FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco

1 Jan 12, 2022

Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

238 Jan 04, 2023

Ontologysim: a Owlready2 library for applied production simulation

Ontologysim: a Owlready2 library for applied production simulation Ontologysim is an open-source deep production simulation framework, with an emphasi

10 Nov 30, 2022

This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection, built on SECOND.

3D-CVF This is the official implementation of 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object

97 Dec 20, 2022