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
Use these libraries to find MULTI-VIEW LEARNING models and implementations
CarFusion
Most implemented papers
Learning Dual Retrieval Module for Semi-supervised Relation Extraction
INK-USC/DualRE
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In this paper, we leverage a key insight that retrieving sentences expressing a relation is a dual task of predicting relation label for a given sentence---two tasks are complementary to each other and can be optimized jointly for mutual enhancement.
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
Deep learning-based, single-view depth estimation methods have recently shown highly promising results.
Deep Multi-View Learning via Task-Optimal CCA
hdcouture/TOCCA
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Canonical Correlation Analysis (CCA) is widely used for multimodal data analysis and, more recently, for discriminative tasks such as multi-view learning; however, it makes no use of class labels.
Multi-View Broad Learning System for Primate Oculomotor Decision Decoding
Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source.
CPM-Nets: Cross Partial Multi-View Networks
hanmenghan/CPM_Nets
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NeurIPS 2019
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.
Dual Adversarial Domain Adaptation
Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely.
Deep Tensor CCA for Multi-view Learning
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data
To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF).
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Object detectors usually achieve promising results with the supervision of complete instance annotations.
