本文的第一作者陈智强，题目为“ SNAP: Shaping Neural Architectures Progressively via Information Density Criterion”，发表于Pattern Recognition

以下为文章摘要：

Excellent neural network architecture is built on the specific target task and device. As the target task or device is different, the neural architecture we need will be different, too. Rather than redesigning or searching a brand new one, adjusting the existing architecture automatically is an alternative yet efficient way. To this end, we propose a method to Shape the existing Neural Architectures Progressively (SNAP) to adapt the target task and device better. Inspired by the streamline of water drop shaped by air resistance, we define an information density criterion (play the role of resistance) to drive the network architecture reducing the size of the part with the lowest information density. Iteratively, a more adaptive architecture will be obtained progressively in a greedy way. Theoretically, we prove that the greedy strategy is reasonable and can shape a better architecture. Because of the small adjustment of architecture each time, new architecture can inherit the parameters in old architecture to avoid retraining it from scratch. So the proposed method is very efficient in no need of high computation cost. Experimental results show that proposed method can effectively improve the given network by adjusting its architecture. And it can generate different architectures for
different tasks and devices to adapt them well. Compared with search-based auto-generated neural architectures, our approach can achieve comparable or even better performance in no need of tremendous computation resources.

文章链接：https://www.sciencedirect.com/science/article/pii/S0031320321001102?via%3Dihub