MULTI-VIEW LEARNING
53 papers with code • 0 benchmarks • 1 datasets
Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.
Source: Dissimilarity-based representation for radiomics applications
Benchmarks
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Libraries
Use these libraries to find MULTI-VIEW LEARNING models and implementations
CarFusion
Latest papers
Trusted Multi-View Classification with Dynamic Evidential Fusion
hanmenghan/TMC
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With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Multi-view Information Bottleneck Without Variational Approximation
archy666/meib
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By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks.
Shared Independent Component Analysis for Multi-Subject Neuroimaging
While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.
Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
Segmentation of images is a long-standing challenge in medical AI.
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition
On the one hand, multiple views across tasks possibly relate to each other under practical situations.
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification
mindspore-ai/models
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CVPR 2021
The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
XLearning-SCU/2021-CVPR-Completer
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CVPR 2021
In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.
Trusted Multi-View Classification
To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Multi-view Temporal Alignment for Non-parallel Articulatory-to-Acoustic Speech Synthesis
Articulatory-to-acoustic (A2A) synthesis refers to the generation of audible speech from captured movement of the speech articulators.
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Object detectors usually achieve promising results with the supervision of complete instance annotations.
