A survey on deep semi-supervised learning
Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …
This paper provides a comprehensive survey on both fundamentals and recent advances in …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
[HTML][HTML] Introducing urdu digits dataset with demonstration of an efficient and robust noisy decoder-based pseudo example generator
In the present work, we propose a novel method utilizing only a decoder for generation of
pseudo-examples, which has shown great success in image classification tasks. The …
pseudo-examples, which has shown great success in image classification tasks. The …
Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data
Deep semi-supervised learning (SSL) methods aim to take advantage of abundant
unlabeled data to improve the algorithm performance. In this paper, we consider the …
unlabeled data to improve the algorithm performance. In this paper, we consider the …
A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear …
H Zhang, C Zhang, J Dong, K Peng - International Journal of …, 2022 - Taylor & Francis
The industrial process monitoring and operating performance assessment techniques are of
great significance to ensure the safety and efficiency of the production and to improve the …
great significance to ensure the safety and efficiency of the production and to improve the …
Hierarchical attentive knowledge graph embedding for personalized recommendation
Abstract Knowledge graphs (KGs) have proven to be effective for high-quality
recommendation, where the connectivities between users and items provide rich and …
recommendation, where the connectivities between users and items provide rich and …
SDL: Spectrum-disentangled representation learning for visible-infrared person re-identification
Visible-infrared person re-identification (RGB-IR ReID) is extremely important for the
surveillance applications under poor illumination conditions. Since the difference in the …
surveillance applications under poor illumination conditions. Since the difference in the …
Learning binary codes with neural collaborative filtering for efficient recommendation systems
The fast-growing e-commerce scenario brings new challenges to traditional collaborative
filtering because the huge amount of users and items requires large storage and efficient …
filtering because the huge amount of users and items requires large storage and efficient …
Evaluation of colorectal cancer subtypes and cell lines using deep learning
Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising
incidence rate in the developed world. Molecular profiling techniques have been used to …
incidence rate in the developed world. Molecular profiling techniques have been used to …
Disentangled-multimodal adversarial autoencoder: Application to infant age prediction with incomplete multimodal neuroimages
Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic
resonance imaging (fMRI) data has the potential to boost the accuracy of infant age …
resonance imaging (fMRI) data has the potential to boost the accuracy of infant age …