LogAvgExp Provides a Principled and Performant Global Pooling Operator

Scott C. Lowe; Thomas Trappenberg; Sageev Oore

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arXiv · 2111.01742

LogAvgExp Provides a Principled and Performant Global Pooling Operator

Scott C. Lowe·Thomas Trappenberg·Sageev Oore

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Abstract

We seek to improve the pooling operation in neural networks, by applying a more theoretically justified operator. We demonstrate that LogSumExp provides a natural OR operator for logits. When one corrects for the number of elements inside the pooling operator, this becomes $\text{LogAvgExp} := \log(\text{mean}(\exp(x)))$. By introducing a single temperature parameter, LogAvgExp smoothly transitions from the max of its operands to the mean (found at the limiting cases $t \to 0^+$ and $t \to +\infty$). We experimentally tested LogAvgExp, both with and without a learnable temperature parameter, in a variety of deep neural network architectures for computer vision.

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Scott C. Lowe, Thomas Trappenberg, Sageev Oore. 2021-11-02. LogAvgExp Provides a Principled and Performant Global Pooling Operator. https://arxiv.org/abs/2111.01742

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