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Found 4 papers, 4 papers with code
Rethinking Multi-view Representation Learning via Distilled Disentangling
To this end, we propose an innovative framework for multi-view representation learning, which incorporates a technique we term 'distilled disentangling'.
Disentangling Multi-view Representations Beyond Inductive Bias
In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion
To this end, in this paper, we proposed a hybrid contrastive fusion algorithm to extract robust view-common representation from unlabeled data.
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