49 citations · 114 across the 18 of their papers we have counts for
7 papers · 1 filter
Unaligned Image-to-Image Translation by Learning to Reweight
Shaoan Xie, Mingming Gong, Yanwu Xu +1
Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumpt…
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Yanwu Xu, Mingming Gong, Shaoan Xie +1
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe ann…
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
Pengfei Wang, Changxing Ding, Wentao Tan +3
Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art obje…
Kernel Mean Estimation by Marginalized Corrupted Distributions
Xiaobo Xia, Shuo Shan, Mingming Gong +4
Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms. Given a finite sample, the standard estimate of the tar…
Instance Correction for Learning with Open-set Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
The problem of open-set noisy labels denotes that part of training data have a different label space that does not contain the true class. Lots of approaches, e.g., loss correction…
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled during training. However, losses are generated on-t…