# Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of Neurons

arXiv: https://arxiv.org/abs/1607.06514 · HTML: https://mlsanity.com/paper/1607.06514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference](https://mlsanity.com/paper/2412.13902/) (2024)

## Abstract (arXiv)

Deep Convolutional Neural Networks (CNNs) are playing important roles in state-of-the-art visual recognition. This paper focuses on modeling the spatial co-occurrence of neuron responses, which is less studied in the previous work. For this, we consider the neurons in the hidden layer as neural words, and construct a set of geometric neural phrases on top of them. The idea that grouping neural words into neural phrases is borrowed from the Bag-of-Visual-Words (BoVW) model. Next, the Geometric Neural Phrase Pooling (GNPP) algorithm is proposed to efficiently encode these neural phrases. GNPP acts as a new type of hidden layer, which punishes the isolated neuron responses after convolution, and can be inserted into a CNN model with little extra computational overhead. Experimental results show that GNPP produces significant and consistent accuracy gain in image classification.