Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree

IEEE Transactions on Pattern Analysis and Machine Intelligence
Chen-Yu LeeZhuowen Tu

Abstract

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling method...Continue Reading

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
Jan 17, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Thomas SerreTomaso Poggio

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Citations

Oct 22, 2020·Sensors·Muhammad Sadiq AminHyunsik Ahn
Dec 29, 2020·Chemical Research in Toxicology·Marcus W H WangTimothy E H Allen
Aug 14, 2021·Computers in Biology and Medicine·Ahmad JahanbakhshiMohammad Momeny

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