PGC-DGCNN provides a new definition of graph convolutional filter. It generalizes the most commonly adopted filter, adding an hyper-parameter controlling the distance of the considered neighborhood. The model extends graph convolutions, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. The methods involves a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph.

Description and image from: [On Filter Size in Graph Convolutional Networks](https://arxiv.org/pdf/1811.10435.pdf)

**Hit-Detector** is a neural architectures search algorithm that simultaneously searches all components of an object detector in an end-to-end manner. It is a hierarchical approach to mine the proper subsearch space from the large volume of operation candidates. It consists of two main procedures. First, given a large search space containing all the operation candidates, we screen out the customized sub search space suitable for each part of detector with the help of group sparsity regularization. Secondly, we search the architectures for each part within the corresponding sub search space by adopting the differentiable manner.

Hit-Detector

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

PGC-DGCNN

On Filter Size in Graph Convolutional Networks

**RealFormer** is a type of [Transformer](https://paperswithcode.com/methods/category/transformers) based on the idea of [residual](https://paperswithcode.com/method/residual-connection) attention. It adds skip edges to the backbone [Transformer](https://paperswithcode.com/method/transformer) to create multiple direct paths, one for each type of attention module. It adds no parameters or hyper-parameters. Specifically, RealFormer uses a Post-[LN](https://paperswithcode.com/method/layer-normalization) style Transformer as backbone and adds skip edges to connect [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention) modules in adjacent layers.

Source	On Filter Size in Graph Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com