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Found 4 papers, 4 papers with code
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data.
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet.
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
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