With the increasing number of new neural architecture designs and substantial
existing neural architectures, it becomes difficult for the researchers to
situate their contributions compared with existing neural architectures or
establish the connections between their designs and other relevant ones. To
discover similar neural architectures in an efficient and automatic manner, we
define a new problem Neural Architecture Retrieval which retrieves a set of
existing neural architectures which have similar designs to the query neural
architecture. Existing graph pre-training strategies cannot address the
computational graph in neural architectures due to the graph size and motifs.
To fulfill this potential, we propose to divide the graph into motifs which are
used to rebuild the macro graph to tackle these issues, and introduce
multi-level contrastive learning to achieve accurate graph representation
learning. Extensive evaluations on both human-designed and synthesized neural
architectures demonstrate the superiority of our algorithm. Such a dataset
which contains 12k real-world network architectures, as well as their
embedding, is built for neural architecture retrieval.

提出了一种新的算法，用于检索与待设计神经结构相似的已存在的神经结构，并引入分层对比学习实现准确定义，它在人工设计和合成神经结构的评价中表现出卓越性能，并建立了包含 12000 个真实世界网络结构及其嵌入的数据集。