Unifying local and non-local signal processing with graph CNNs
Not on the map. Why?
Abstract (arXiv)
This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3.
3 papers.
- 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
- 2021Generalization bounds for graph convolutional neural networks via Rademacher complexitycited by 17
- 2021Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequencycited by 0
- 2021Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency19 Apr 2021
- 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systems19 Apr 2021
- 2021Generalization bounds for graph convolutional neural networks via Rademacher complexity20 Feb 2021