Combating noisy labels by agreement: A joint training method with co-regularization
Deep Learning with noisy labels is a practically challenging problem in weakly-supervised
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …
Provably consistent partial-label learning
Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …
example is associated with a set of candidate labels. Even though many practical PLL …
Boosting deep learning risk prediction with generative adversarial networks for electronic health records
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
Stacked autoencoders driven by semi-supervised learning for building extraction from near infrared remote sensing imagery
In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-
Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near …
Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near …
Dynamic graph co-matching for unsupervised video-based person re-identification
Cross-camera label estimation from a set of unlabeled training data is an extremely
important component in the unsupervised person re-identification (re-ID) systems. With the …
important component in the unsupervised person re-identification (re-ID) systems. With the …
Enhance High Impedance Fault Detection and Location Accuracy via -PMUs
The high impedance fault (HIF) has random, irregular, and unsymmetrical characteristics,
making such a fault difficult to detect in distribution grids via conventional relay …
making such a fault difficult to detect in distribution grids via conventional relay …
Learning from complementary labels
Collecting labeled data is costly and thus a critical bottleneck in real-world classification
tasks. To mitigate this problem, we propose a novel setting, namely learning from …
tasks. To mitigate this problem, we propose a novel setting, namely learning from …
Learning with multiple complementary labels
A complementary label (CL) simply indicates an incorrect class of an example, but learning
with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the …
with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the …
An information-theoretical approach to semi-supervised learning under covariate-shift
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test
data are drawn from the same distribution. However, this assumption is not satisfied in many …
data are drawn from the same distribution. However, this assumption is not satisfied in many …
Semi-supervised classification based on classification from positive and unlabeled data
Most of the semi-supervised classification methods developed so far use unlabeled data for
regularization purposes under particular distributional assumptions such as the cluster …
regularization purposes under particular distributional assumptions such as the cluster …