Mixed Pooling for Convolutional Neural Networks

By Dingjun Yu, Hanli Wang, Peiqiu Chen, Zhihua Wei. 2014

425 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

Recent advances in convolutional neural networks (2015, background): “Also, it may slow down the training process due to the constant zero gradients.”
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, methodology): “There are various pooling methods, including Max Pooling, Average Pooling, Spatial Pyramid Pooling, Mixed Pooling, Multi-Scale Order-Less, and Stochastic Pooling [ 21-24 ].”
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017, background): “…5.4.2), Yu, Wang, Chen, and Wei (2014) introduced a novel mixed pooling technique to further boost the regularization abilities of DCNNs and address the known issues associated with average and max pooling (Zeiler & Fergus, 2013; D. Yu et al., 2014; Sainath, Kingsbury, Mohamed et al., 2013).”
Pooling Methods in Deep Neural Networks, a Review (2020, methodology): “[13] proposed a hybrid approach by combining the average pooling and max pooling.”
Review of Image Classification Algorithms Based on Convolutional Neural Networks (2021, methodology): “Although there are max pooling and average pooling, some methods that can more effectively alleviate the over-fitting of convolutional neural networks are proposed, such as Lp Pooling [67], Mixed Pooling [68], Stochastic Pooling [69], Spatial Pyramid Pooling (SPP) [70] and Multi-scale Orderless Pool”
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection (2023, methodology): “Thus, we employ a method aggregating average pooling and max pooling adaptively inspired from mixed pooling [41], as shown in Eq.”
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “5 and 6) might lead to a loss of high-order information, as different channels would learn embeddings with varying distributions on different hypergraphs [54].”
Capsule Networks for Hyperspectral Image Classification (2019, methodology): “age, or sum operation (among other recent methods, such as mixed pooling [47], stochastic pooling [48], or wavelet”
Recent Advances in Convolutional Neural Network Acceleration (2018, methodology): “In spite of max pooling and average pooling, some methods that work better in mitigating overfitting problems in CNN are proposed such as Lp pooling [38], stochastic pooling [39], and mixed pooling [40].”