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08/26/2023
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples
Sample selection is a prevalent method in learning with noisy labels, wh...
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08/12/2023
Multi-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the ...
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07/14/2023
Exploiting Counter-Examples for Active Learning with Partial labels
This paper studies a new problem, active learning with partial labels (A...
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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...
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06/20/2023
A Universal Unbiased Method for Classification from Aggregate Observations
In conventional supervised classification, true labels are required for ...
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06/18/2023
Weakly Supervised Regression with Interval Targets
This paper investigates an interesting weakly supervised regression sett...
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06/15/2023
Partial-Label Regression
Partial-label learning is a popular weakly supervised learning setting t...
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04/18/2023
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
Learning-based point cloud registration methods can handle clean point c...
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03/27/2023
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Estimating the generalization performance is practically challenging on ...
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03/18/2023
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
Partial-label learning (PLL) is an important weakly supervised learning ...
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03/04/2023
Fine-Grained Classification with Noisy Labels
Learning with noisy labels (LNL) aims to ensure model generalization giv...
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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...
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12/08/2022
Logit Clipping for Robust Learning against Label Noise
In the presence of noisy labels, designing robust loss functions is crit...
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07/21/2022
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
The ability to train deep neural networks under label noise is appealing...
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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...
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05/19/2022
Mitigating Neural Network Overconfidence with Logit Normalization
Detecting out-of-distribution inputs is critical for safe deployment of ...
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01/31/2022
Can Adversarial Training Be Manipulated By Non-Robust Features?
Adversarial training, originally designed to resist test-time adversaria...
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01/22/2022
PiCO: Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each tr...
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01/16/2022
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
This paper studies weakly supervised domain adaptation(WSDA) problem, wh...
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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 ...
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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? ...
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06/11/2021
On the Robustness of Average Losses for Partial-Label Learning
Partial-label (PL) learning is a typical weakly supervised classificatio...
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02/13/2021
Learning from Similarity-Confidence Data
Weakly supervised learning has drawn considerable attention recently to ...
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02/09/2021
Provable Defense Against Delusive Poisoning
Delusive poisoning is a special kind of attack to obstruct learning, whe...
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12/09/2020
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
Deep neural networks have been shown to easily overfit to biased trainin...
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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 ...
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10/05/2020
Pointwise Binary Classification with Pairwise Confidence Comparisons
Ordinary (pointwise) binary classification aims to learn a binary classi...
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07/28/2020
COMET: Convolutional Dimension Interaction for Deep Matrix Factorization
Latent factor models play a dominant role among recommendation technique...
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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...
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07/17/2020
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, wh...
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04/17/2020
Incorporating Multiple Cluster Centers for Multi-Label Learning
Multi-label learning deals with the problem that each instance is associ...
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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...
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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 ...
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02/19/2020
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning is one of the important weakly supervised learnin...
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12/30/2019
Learning from Multiple Complementary Labels
Complementary-label learning is a new weakly-supervised learning framewo...
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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...
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02/08/2019
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially importa...
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02/08/2019
