ArcText: An Unified Text Approach to Describing Convolutional Neural Network Architectures
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Numerous Convolutional Neural Network (CNN) models have demonstrated their promising performance mostly in computer vision. The superiority of CNNs mainly relies on their complex architectures that are often manually designed with extensive human expertise. Data mining on CNN architectures can discover useful patterns and fundamental sub-comments from existing CNN architectures, providing common researchers with strong prior knowledge to design 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 rare work that has been used for this aspect. The main reason behind this is the barrier between CNN architectures and data mining algorithms. Specifically, the current CNN architecture descriptions cannot be exactly vectorized to the input to data mining algorithms. In this paper, we propose a unified approach, named ArcTxt, to describing CNN architectures based on text. Particularly, three different units of ArcText and an order method have been elaborately designed, to uniquely describe the same architecture including the sufficient information. Also, the resulted description can also be exactly converted back to the corresponding CNN architecture. ArcText bridge the gap between CNN and data mining researchers, and has the potentiality to be utilized to wider scenarios.
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