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MULTI-VIEW LEARNING

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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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/cdal/dcmf

공식 코드

Deep Collective Matrix Factorization for Augmented Multi-View Learning

https://github.com/Atik-Ahamed/TSCMamba

공식 코드

TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification

https://github.com/FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification

공식 코드

Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

https://github.com/HongtengXu/Relational-AutoEncoders

공식 코드

Learning Autoencoders with Relational Regularization

https://github.com/INK-USC/DualRE

공식 코드

Learning Dual Retrieval Module for Semi-supervised Relation Extraction

https://github.com/IoBT-VISTEC/SleepPoseNet

공식 코드

SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

https://github.com/PengxinZeng/2023-TPAMI-SMILE

공식 코드

Semantic Invariant Multi-view Clustering with Fully Incomplete Information

https://github.com/SMehrkanoon/Deep-brain-state-classification-of-MEG-data

공식 코드

Deep brain state classification of MEG data

https://github.com/Willy1005/2025-IJCAI-RDML

공식 코드

Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks

https://github.com/XLearning-SCU/2023-IJCAI-ProImp

공식 코드

Incomplete Multi-view Clustering via Prototype-based Imputation

https://github.com/YilinZhang107/TMNR

공식 코드

Trusted Multi-view Learning with Label Noise

https://github.com/ZeusDavide/TAI_2021_ANIMC

공식 코드

ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering

https://github.com/ZeusDavide/TAI_V3H

공식 코드

V3H: View Variation and View Heredity for Incomplete Multi-view Clustering

https://github.com/ZeusDavide/TETCI_UIMC

공식 코드

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

https://github.com/ZhenhuaShi/MvBLS

공식 코드

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

https://github.com/akuehlka/emvlc-ipad

공식 코드

Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

https://github.com/alibabaresearch/damo-convai

공식 코드

Fine-Tuning Language Models with Reward Learning on Policy

https://github.com/an-michaelg/rt4u

공식 코드

Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography

https://github.com/archy666/cumi

공식 코드

Discovering Common Information in Multi-view Data

https://github.com/archy666/meib

공식 코드

Multi-view Information Bottleneck Without Variational Approximation

https://github.com/bezirganyan/luma

공식 코드

LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data

https://github.com/darrenzzhang/survey_imc

공식 코드

A Survey on Incomplete Multi-view Clustering

https://github.com/daviddongkc/smile_oie

공식 코드

Syntactic Multi-view Learning for Open Information Extraction

https://github.com/dushide/openviewer

공식 코드

OpenViewer: Openness-Aware Multi-View Learning

https://github.com/fdudsde/www2023consrec

공식 코드

ConsRec: Learning Consensus Behind Interactions for Group Recommendation

https://github.com/fmenat/com-views

공식 코드

Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach

https://github.com/fmenat/missingviews-study-eo

공식 코드

Impact Assessment of Missing Data in Model Predictions for Earth Observation Applications

https://github.com/fmenat/multiviewRS-models

공식 코드

Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications

https://github.com/fmenat/multiviewcropclassification

공식 코드

A Comparative Assessment of Multi-view fusion learning for Crop Classification

https://github.com/fmenat/mvlearning

공식 코드

Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation

https://github.com/fmenat/optimal-multiview-crop-classifier

공식 코드

Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction

https://github.com/guanlueli/AMDiff

공식 코드

Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model

https://github.com/guanyuezhen/bmvc

공식 코드

Balanced Multi-view Clustering

https://github.com/guanyuezhen/mask-imvc

공식 코드

Mask-informed Deep Contrastive Incomplete Multi-view Clustering

https://github.com/guanzhou-ke/cloven

공식 코드

A Clustering-guided Contrastive Fusion for Multi-view Representation Learning

https://github.com/hanmenghan/TMC

공식 코드

Trusted Multi-View Classification

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