This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …

Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

Network embedding is an important method to learn low-dimensional representations of
vertexes in networks, aiming to capture and preserve the network structure. Almost all the …

Multi-view graph-based clustering aims to provide clustering solutions to multi-view data.
However, most existing methods do not give sufficient consideration to weights of different …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting
multiple views of data. However, most existing multi-view clustering methods only aim to …

Most existing approaches address multi-view subspace clustering problem by constructing
the affinity matrix on each view separately and afterwards propose how to extend spectral …

Training thin deep networks following the student-teacher learning paradigm has received
intensive attention because of its excellent performance. However, to the best of our …

Multiview data clustering attracts more attention than their single-view counterparts due to
the fact that leveraging multiple independent and complementary information from multiview …