b'Lei Feng'

research

∙ 08/26/2023

Late Stopping: Avoiding Confidently Learning from Mislabeled Examples

Sample selection is a prevalent method in learning with noisy labels, wh...

0 Suqin Yuan, et al. ∙

research

∙ 08/12/2023

Multi-Label Knowledge Distillation

Existing knowledge distillation methods typically work by imparting the ...

0 Penghui Yang, et al. ∙

research

∙ 07/14/2023

Exploiting Counter-Examples for Active Learning with Partial labels

This paper studies a new problem, active learning with partial labels (A...

0 Fei Zhang, et al. ∙

research

∙ 07/02/2023

Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples

Partial-label learning (PLL) relies on a key assumption that the true la...

0 Shuo He, et al. ∙

research

∙ 06/20/2023

A Universal Unbiased Method for Classification from Aggregate Observations

In conventional supervised classification, true labels are required for ...

0 Zixi Wei, et al. ∙

research

∙ 06/18/2023

Weakly Supervised Regression with Interval Targets

This paper investigates an interesting weakly supervised regression sett...

0 Xin Cheng, et al. ∙

research

∙ 06/15/2023

Partial-Label Regression

Partial-label learning is a popular weakly supervised learning setting t...

0 Xin Cheng, et al. ∙

research

∙ 04/18/2023

SDFReg: Learning Signed Distance Functions for Point Cloud Registration

Learning-based point cloud registration methods can handle clean point c...

0 Leida Zhang, et al. ∙

research

∙ 03/27/2023

On the Importance of Feature Separability in Predicting Out-Of-Distribution Error

Estimating the generalization performance is practically challenging on ...

0 Renchunzi Xie, et al. ∙

research

∙ 03/18/2023

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning

Partial-label learning (PLL) is an important weakly supervised learning ...

0 Shiyu Tian, et al. ∙

research

∙ 03/04/2023

Fine-Grained Classification with Noisy Labels

Learning with noisy labels (LNL) aims to ensure model generalization giv...

0 Qi Wei, et al. ∙

research

∙ 01/28/2023

DALI: Dynamically Adjusted Label Importance for Noisy Partial Label Learning

Noisy partial label learning (noisy PLL) is an important branch of weakl...

0 Mingyu Xu, et al. ∙

research

∙ 12/08/2022

Logit Clipping for Robust Learning against Label Noise

In the presence of noisy labels, designing robust loss functions is crit...

0 Hongxin Wei, et al. ∙

research

∙ 07/21/2022

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

The ability to train deep neural networks under label noise is appealing...

0 Haobo Wang, et al. ∙

research

∙ 06/17/2022

Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets

Deep neural networks usually perform poorly when the training dataset su...

0 Hongxin Wei, et al. ∙

research

∙ 05/19/2022

Mitigating Neural Network Overconfidence with Logit Normalization

Detecting out-of-distribution inputs is critical for safe deployment of ...

0 Hongxin Wei, et al. ∙

research

∙ 01/31/2022

Can Adversarial Training Be Manipulated By Non-Robust Features?

Adversarial training, originally designed to resist test-time adversaria...

0 Lue Tao, et al. ∙

research

∙ 01/22/2022

PiCO: Contrastive Label Disambiguation for Partial Label Learning

Partial label learning (PLL) is an important problem that allows each tr...

0 Haobo Wang, et al. ∙

research

∙ 01/16/2022

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

This paper studies weakly supervised domain adaptation(WSDA) problem, wh...

0 Renchunzi Xie, et al. ∙

research

∙ 11/26/2021

Edge computing for cyber-physical systems: A systematic mapping study emphasizing trustworthiness

Edge computing is projected to have profound implications in the coming ...

0 José Manuel Gaspar Sánchez, et al. ∙

research

∙ 06/16/2021

Multi-Class Classification from Single-Class Data with Confidences

Can we learn a multi-class classifier from only data of a single class? ...

0 Yuzhou Cao, et al. ∙

research

∙ 06/11/2021

On the Robustness of Average Losses for Partial-Label Learning

Partial-label (PL) learning is a typical weakly supervised classificatio...

0 Jiaqi Lv, et al. ∙

research

∙ 02/13/2021

Learning from Similarity-Confidence Data

Weakly supervised learning has drawn considerable attention recently to ...

0 Yuzhou Cao, et al. ∙

research

∙ 02/09/2021

Provable Defense Against Delusive Poisoning

Delusive poisoning is a special kind of attack to obstruct learning, whe...

5 Lue Tao, et al. ∙

research

∙ 12/09/2020

MetaInfoNet: Learning Task-Guided Information for Sample Reweighting

Deep neural networks have been shown to easily overfit to biased trainin...

0 Hongxin Wei, et al. ∙

research

∙ 12/02/2020

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

Deep learning with noisy labels is a challenging task. Recent prominent ...

0 Zhuowei Wang, et al. ∙

research

∙ 10/05/2020

Pointwise Binary Classification with Pairwise Confidence Comparisons

Ordinary (pointwise) binary classification aims to learn a binary classi...

3 Lei Feng, et al. ∙

research

∙ 07/28/2020

COMET: Convolutional Dimension Interaction for Deep Matrix Factorization

Latent factor models play a dominant role among recommendation technique...

0 Zhuoyi Lin, et al. ∙

research

∙ 07/28/2020

GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

Graph-based recommendation models work well for top-N recommender system...

0 Zhuoyi Lin, et al. ∙

research

∙ 07/17/2020

Provably Consistent Partial-Label Learning

Partial-label learning (PLL) is a multi-class classification problem, wh...

5 Lei Feng, et al. ∙

research

∙ 04/17/2020

Incorporating Multiple Cluster Centers for Multi-Label Learning

Multi-label learning deals with the problem that each instance is associ...

6 Senlin Shu, et al. ∙

research

∙ 03/31/2020

Learning Cross-domain Semantic-Visual Relation for Transductive Zero-Shot Learning

Zero-Shot Learning (ZSL) aims to learn recognition models for recognizin...

0 Jianyang Zhang, et al. ∙

research

∙ 03/05/2020

Combating noisy labels by agreement: A joint training method with co-regularization

Deep Learning with noisy labels is a practically challenging problem in ...

0 Hongxin Wei, et al. ∙

research

∙ 02/19/2020

Progressive Identification of True Labels for Partial-Label Learning

Partial-label learning is one of the important weakly supervised learnin...

0 Jiaqi Lv, et al. ∙

research

∙ 12/30/2019

Learning from Multiple Complementary Labels

Complementary-label learning is a new weakly-supervised learning framewo...

0 Lei Feng, et al. ∙

research

∙ 04/22/2019

A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification

Results from multiple diagnostic tests are usually combined to improve t...

0 Raju Maiti, et al. ∙

research

∙ 02/08/2019

Collaboration based Multi-Label Learning

It is well-known that exploiting label correlations is crucially importa...

0 Lei Feng, et al. ∙

research

∙ 02/08/2019

Partial Label Learning with Self-Guided Retraining

Partial label learning deals with the problem where each training instan...

0 Lei Feng, et al. ∙

Lei Feng

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