Gated Graph Sequence Neural Networks (GGS-NNs) is a novel graph-based neural network model. GGS-NNs modifies Graph Neural Networks (Scarselli et al., 2009) to use gated recurrent units and modern optimization techniques and then extend to output sequences.

Source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

Image source: [Li et al.](https://arxiv.org/pdf/1511.05493v4.pdf)

**DeepLab** is a semantic segmentation architecture. First, the input image goes through the network with the use of dilated convolutions. Then the output from the network is bilinearly interpolated and goes through the fully connected [CRF](https://paperswithcode.com/method/crf) to fine tune the result we obtain the final predictions.

DeepLab

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

GGS-NNs

Gated Graph Sequence Neural Networks

**Mobile Neural Network (MNN)** is a mobile inference engine tailored to mobile applications. The contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2) delivering thorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight.

Source	Gated Graph Sequence Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com