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Found 9 papers, 1 papers with code
Inclusive normalization of face images to passport format
In this work, a style based face normalization model (StyleFNM) is proposed to remove most intra-personal variations including large changes in pose, bad or harsh illumination, low resolution, blur, facial expressions, and accessories like sunglasses among others.
Multi-view user representation learning for user matching without personal information
To deal with these challenges, we propose the similarity based multi-view information fusion to learn a better user representation from URLs by treating the URLs as multi-view data.
Towards more sustainable enterprise data and application management with cross silo Federated Learning and Analytics
To comply with new legal requirements and policies committed to privacy protection, more and more companies start to deploy cross-silo Federated Learning at global scale, where several clients/silos collaboratively train a global model under the coordination of a central server.
Destination similarity based on implicit user interest
In this work, a new similarity method is proposed to measure the destination similarity in terms of implicit user interest.
Random Forest for Dissimilarity-based Multi-view Learning
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions.
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task.
Dynamic voting in multi-view learning for radiomics applications
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients.
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning.
Dissimilarity-based representation for radiomics applications
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.
