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Found 3 papers, 0 papers with code
Learning to Generate Parameters of ConvNets for Unseen Image Data
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e. g., SGD or Adam) to learn network parameters, which makes training very time- and resource-intensive.
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models.
