  The superiority of Convolutional Neural Networks (CNNs) largely relies on
their architectures that are often manually crafted with extensive human
expertise. Unfortunately, such kind of domain knowledge is not necessarily
owned by each of the users interested. Data mining on existing CNN can discover
useful patterns and fundamental sub-comments from their architectures,
providing researchers with strong prior knowledge to design proper CNN
architectures when they have no expertise in CNNs. There have been various
state-of-the-art data mining algorithms at hand, while there is only rare work
that has been done for the mining. One of the main reasons is the gap between
CNN architectures and data mining algorithms. Specifically, the current CNN
architecture descriptions cannot be exactly vectorized to the input of data
mining algorithms. In this paper, we propose a unified approach, named ArcText,
to describing CNN architectures based on text. Particularly, four different
units and an ordering method have been elaborately designed in ArcText, to
uniquely describe the same architecture with sufficient information. Also, the
resulted description can be exactly converted back to the corresponding CNN
architecture. ArcText bridges the gap between CNN architectures and data mining
researchers, and has the potentiality to be utilized to wider scenarios.
