MULTI-VIEW LEARNING
49 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
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Use these libraries to find MULTI-VIEW LEARNING models and implementationsLatest papers
Reliable Conflictive Multi-View Learning
To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data.
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
The Canonical Correlation Analysis (CCA) family of methods is foundational in multiview learning.
Explainable Multi-View Deep Networks Methodology for Experimental Physics
In this paper, we suggest different multi-view architectures for the vision domain, each suited to another problem, and we also present a methodology for explaining these models.
A Comparative Assessment of Multi-view fusion learning for Crop Classification
Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance.
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
Semantic Invariant Multi-view Clustering with Fully Incomplete Information
To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.
Dual Contrastive Prediction for Incomplete Multi-view Representation Learning
In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views.
Siamese DETR
In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR.
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.