Xiaobo Xia | DeepAI

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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...

0 Xiaobo Xia, et al. ∙

∙ 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...

0 Yuhao Wu, et al. ∙

∙ 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...

0 Shikun Li, et al. ∙

∙ 03/23/2023

Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

Robust generalization aims to tackle the most challenging data distribut...

0 Zhuo Huang, et al. ∙

∙ 03/21/2023

Dynamic-Aware Loss for Learning with Label Noise

Label noise poses a serious threat to deep neural networks (DNNs). Emplo...

0 Xiu-Chuan Li, et al. ∙

∙ 02/07/2023

HumanMAC: Masked Motion Completion for Human Motion Prediction

Human motion prediction is a classical problem in computer vision and co...

0 Ling-Hao Chen, et al. ∙

∙ 07/07/2022

Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

Machine learning models are vulnerable to Out-Of-Distribution (OOD) exam...

0 Zhuo Huang, et al. ∙

∙ 05/18/2022

Pluralistic Image Completion with Probabilistic Mixture-of-Experts

Pluralistic image completion focuses on generating both visually realist...

4 Xiaobo Xia, et al. ∙

∙ 03/08/2022

Selective-Supervised Contrastive Learning with Noisy Labels

Deep networks have strong capacities of embedding data into latent repre...

0 Shikun Li, et al. ∙

∙ 10/06/2021

Objects in Semantic Topology

A more realistic object detection paradigm, Open-World Object Detection,...

0 Shuo Yang, et al. ∙

∙ 07/10/2021

Kernel Mean Estimation by Marginalized Corrupted Distributions

Estimating the kernel mean in a reproducing kernel Hilbert space is a cr...

0 Xiaobo Xia, et al. ∙

∙ 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 ...

0 Xiaobo Xia, et al. ∙

∙ 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...

0 Xiaobo Xia, et al. ∙

∙ 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...

0 Xiaobo Xia, et al. ∙

∙ 06/14/2020

Parts-dependent Label Noise: Towards Instance-dependent Label Noise

Learning with the instance-dependent label noise is challenging, because...

0 Xiaobo Xia, et al. ∙

∙ 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...

0 Songhua Wu, et al. ∙

∙ 02/16/2020

Multi-Class Classification from Noisy-Similarity-Labeled Data

A similarity label indicates whether two instances belong to the same cl...

9 Songhua Wu, et al. ∙

∙ 06/01/2019

Are Anchor Points Really Indispensable in Label-Noise Learning?

In label-noise learning, noise transition matrix, denoting the probabili...

0 Xiaobo Xia, et al. ∙

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