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09/02/2023
Regularly Truncated M-estimators for Learning with Noisy Labels
The sample selection approach is very popular in learning with noisy lab...
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06/12/2023
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Training a classifier exploiting a huge amount of supervised data is exp...
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06/05/2023
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
Learning from crowds describes that the annotations of training data are...
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03/23/2023
Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization
Robust generalization aims to tackle the most challenging data distribut...
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03/21/2023
Dynamic-Aware Loss for Learning with Label Noise
Label noise poses a serious threat to deep neural networks (DNNs). Emplo...
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02/07/2023
HumanMAC: Masked Motion Completion for Human Motion Prediction
Human motion prediction is a classical problem in computer vision and co...
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07/07/2022
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Machine learning models are vulnerable to Out-Of-Distribution (OOD) exam...
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05/18/2022
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Pluralistic image completion focuses on generating both visually realist...
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03/08/2022
Selective-Supervised Contrastive Learning with Noisy Labels
Deep networks have strong capacities of embedding data into latent repre...
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10/06/2021
Objects in Semantic Topology
A more realistic object detection paradigm, Open-World Object Detection,...
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07/10/2021
Kernel Mean Estimation by Marginalized Corrupted Distributions
Estimating the kernel mean in a reproducing kernel Hilbert space is a cr...
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06/01/2021
Instance Correction for Learning with Open-set Noisy Labels
The problem of open-set noisy labels denotes that part of training data ...
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06/01/2021
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
In learning with noisy labels, the sample selection approach is very pop...
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12/02/2020
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
The label noise transition matrix T, reflecting the probabilities that t...
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06/14/2020
Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the instance-dependent label noise is challenging, because...
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06/14/2020
Class2Simi: A New Perspective on Learning with Label Noise
Label noise is ubiquitous in the era of big data. Deep learning algorith...
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02/16/2020
Multi-Class Classification from Noisy-Similarity-Labeled Data
A similarity label indicates whether two instances belong to the same cl...
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06/01/2019
