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Differentiable Information Bottleneck for Deterministic Multi-view Clustering
In this work, we propose a new differentiable information bottleneck (DIB) method, which provides a deterministic and analytical MVC solution by fitting the mutual information without the necessity of variational approximation.
Live and Learn: Continual Action Clustering with Incremental Views
Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized.
Multi-Task Clustering of Human Actions by Sharing Information
In this work, we present a novel and effective Multi-Task Information Bottleneck (MTIB) clustering method, which is capable of exploring the shared information between multiple action clustering tasks to improve the performance of individual task.
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