Deep Learning on Irregular Domains (DLID2018)
Paper submission details:
Advances in learning spatially related features via convolutional neural network (CNN) architectures has resulted in strong performance gains within the image understanding domain. Many real world problems do not exhibit such a regular spatial domain, making it non-trivial to define a feature mining operator. Such domains may still exhibit spatial relationships that may be of use for learning; weather stations across a country, or joints on the human skeleton for example. The area of deep learning on irregular domains has attempted to make use of the intrinsic spatial information encoded in the domain to learn features on the problem at hand. They employ such methods as signal processing on the graph, graph-based CNNs and manifold-based heat kernels to learn a filtering on input data from a localised region of the domain.
This workshop aims to foster study into the understanding of implementing deep learning on spaces in which conventional CNN operations are ill-defined. It is hoped that the community will be able to engage in dedicated discussions into advancing state of the art performance for problem domains with an irregular topology.
Topics of interest include, but are not limited to:
- Exploiting spatial relationships in non-Euclidean domains
- Data mining and signal processing on graphs
- Spectral graph methods
- Deep learning on irregular problems and graph structured data
- Feature extraction on graphs
- Applications of deep learning on novel domains
- Developing filters on manifolds, graphs and non-Euclidean spaces
- Learning domain topology
- Graph construction and pooling
- Domains with an irregular spatial relationships that would benefit from feature mining.
Paper Submission date:13th July 2018
Paper Acceptance Notification:20th July 2018
Camera Ready Submission:27th July 2018
Author Registration:27th July 2018
Workshop Event:6th September 2018