MULTI-VIEW LEARNING
52 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
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CarFusion
Latest papers with no code
Approaching human 3D shape perception with neurally mappable models
Finally, we find that while the models trained with multi-view learning objectives are able to partially generalize to new object categories, they fall short of human alignment.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning.
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods
Though I focus on speech data, the methods described in this thesis can also be applied to other domains.
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging
Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.
MultiEarth 2023 Deforestation Challenge -- Team FOREVER
It is important problem to accurately estimate deforestation of satellite imagery since this approach can analyse extensive area without direct human access.
Multi-View Class Incremental Learning
Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance.
One-step Multi-view Clustering with Diverse Representation
In light of this, we propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework.
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness
The DualHGNN first leverages a multi-view hypergraph learning network to explore the optimal hypergraph structure from multiple views, constrained by a consistency loss proposed to improve its generalization.
Learning Reliable Representations for Incomplete Multi-View Partial Multi-Label Classification
The application of multi-view contrastive learning has further facilitated this process, however, the existing multi-view contrastive learning methods crudely separate the so-called negative pair, which largely results in the separation of samples belonging to the same category or similar ones.
MetaViewer: Towards A Unified Multi-View Representation
To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner.
