This paper introduces AutoGCN, a generic Neural Architecture Search (NAS)
algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks
(GCNs). HAR has gained attention due to advances in deep learning, increased
data availability, and enhanced computational capabilities. At the same time,
GCNs have shown promising results in modeling relationships between body key
points in a skeletal graph. While domain experts often craft dataset-specific
GCN-based methods, their applicability beyond this specific context is severely
limited. AutoGCN seeks to address this limitation by simultaneously searching
for the ideal hyperparameters and architecture combination within a versatile
search space using a reinforcement controller while balancing optimal
exploration and exploitation behavior with a knowledge reservoir during the
search process. We conduct extensive experiments on two large-scale datasets
focused on skeleton-based action recognition to assess the proposed algorithm's
performance. Our experimental results underscore the effectiveness of AutoGCN
in constructing optimal GCN architectures for HAR, outperforming conventional
NAS and GCN methods, as well as random search. These findings highlight the
significance of a diverse search space and an expressive input representation
to enhance the network performance and generalizability.

AutoGCN 通过深度学习、数据可用性提高以及增强的计算能力，利用图卷积网络（GCNs）对人体活动识别（HAR）进行神经架构搜索（NAS），在搜索过程中通过知识储备库平衡最佳探索和利用行为，提供了多样的搜索空间和高度表达的输入表示，以增强网络性能和泛化能力。

AutoGCN：基于神经构架搜索的通用人体活动识别

AutoGCN -- Towards Generic Human Activity Recognition with Neural  Architecture Search

Graph convolutional networks are becoming indispensable for deep learning
from graph-structured data. Most of the existing graph convolutional networks
share two big shortcomings. First, they are essentially low-pass filters, thus
the potentially useful middle and high frequency band of graph signals are
ignored. Second, the bandwidth of existing graph convolutional filters is
fixed. Parameters of a graph convolutional filter only transform the graph
inputs without changing the curvature of a graph convolutional filter function.
In reality, we are uncertain about whether we should retain or cut off the
frequency at a certain point unless we have expert domain knowledge. In this
paper, we propose Automatic Graph Convolutional Networks (AutoGCN) to capture
the full spectrum of graph signals and automatically update the bandwidth of
graph convolutional filters. While it is based on graph spectral theory, our
AutoGCN is also localized in space and has a spatial form. Experimental results
show that AutoGCN achieves significant improvement over baseline methods which
only work as low-pass filters.

本文提出了 Automatic Graph Convolutional Networks (AutoGCN) 来捕获图信号的全部频谱，并自动更新图卷积滤波器的带宽，实验结果表明，AutoGCN 比只能作为低通滤波器的基准方法有了显著的改进。