Convolutional Neural Networks have revolutionized vision applications. There
are image domains and representations, however, that cannot be handled by
standard CNNs (e.g., spherical images, superpixels). Such data are usually
processed using networks and algorithms specialized for each type. In this
work, we show that it may not always be necessary to use specialized neural
networks to operate on such spaces. Instead, we introduce a new structured
graph convolution operator that can copy 2D convolution weights, transferring
the capabilities of already trained traditional CNNs to our new graph network.
This network can then operate on any data that can be represented as a
positional graph. By converting non-rectilinear data to a graph, we can apply
these convolutions on these irregular image domains without requiring training
on large domain-specific datasets. Results of transferring pre-trained image
networks for segmentation, stylization, and depth prediction are demonstrated
for a variety of such data forms.

本论文提出了一种新的结构化图卷积算子，可以复制二维卷积权重，将传统 CNN 训练的能力转移到新的图网络，从而可以处理非矩形数据，不需要在大型数据集上进行领域特定的训练。作者还展示了将预训练图像网络应用于分割、风格化和深度预测等多种数据形式，并得出了相应的实验结果。

SelectionConv: 针对非矩形图像数据的卷积神经网络

SelectionConv: Convolutional Neural Networks for Non-rectilinear Image  Data

In this paper, we introduce a generalized value iteration network (GVIN),
which is an end-to-end neural network planning module. GVIN emulates the value
iteration algorithm by using a novel graph convolution operator, which enables
GVIN to learn and plan on irregular spatial graphs. We propose three novel
differentiable kernels as graph convolution operators and show that the
embedding based kernel achieves the best performance. We further propose
episodic Q-learning, an improvement upon traditional n-step Q-learning that
stabilizes training for networks that contain a planning module. Lastly, we
evaluate GVIN on planning problems in 2D mazes, irregular graphs, and
real-world street networks, showing that GVIN generalizes well for both
arbitrary graphs and unseen graphs of larger scale and outperforms a naive
generalization of VIN (discretizing a spatial graph into a 2D image).

该论文介绍了一个称为 GVIN 的神经网络规划模块，利用图卷积算子模拟价值迭代算法，并提出了三种不同可微分的卷积核，其中基于嵌入的卷积核表现最佳，进而提出了改进型 Q 学习，用于网络训练的稳定化。实验结果表明，GVIN 在二维迷宫、不规则图形和真实街道网络的规划问题上表现出优秀的泛化性能。