  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.
