<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.</p></abstract>

Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.

Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

National Institute of Technical Teachers’ Training and Research (NITTTR)

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN....

Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

Data publikacji: 28 sie 2019

Zakres stron: 303 - 330

Otrzymano: 22 wrz 2018

Przyjęty: 29 kwi 2019

DOI: https://doi.org/10.2478/fcds-2019-0016

Słowa kluczowe
Pooling strategies, convolutional neural network, visual recognition, regularization, overfitting

© 2019 Shallu Sharma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Implications of Pooling Strategies in Convolutional Neural Networks: A Deep Insight

Data publikacji: 28 sie 2019

Zakres stron: 303 - 330

Otrzymano: 22 wrz 2018

Przyjęty: 29 kwi 2019

DOI: https://doi.org/10.2478/fcds-2019-0016

Słowa kluczowePooling strategies, convolutional neural network, visual recognition, regularization, overfitting

© 2019 Shallu Sharma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Pooling strategies, convolutional neural network, visual recognition, regularization, overfitting