Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Yahya Doğan

Makale detayları

Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Yazarlar:

Yahya Doğan

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 1, pp.95-117

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001304931

pISSN:

1303-6009

Anahtar kelimeler:

Özet:

 Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. 2x2, 3x3, 10x10. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat Max, Avg, to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

 Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. 2x2, 3x3, 10x10. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat Max, Avg, to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

 Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. 2x2, 3x3, 10x10. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat Max, Avg, to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

 Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. 2x2, 3x3, 10x10. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat Max, Avg, to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

Makale detayları

Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Yazarlar:

Yahya Doğan

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 1, pp.95-117

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001304931

pISSN:

1303-6009

Pooling

deep learning

convolutional neural network

Sayı

Makale detayları

Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Yazarlar: Yahya Doğan

Yayıncı: Ankara University

Ana Başlıklar: Information Systems (Other)

Dergi: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 1, pp.95-117

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001304931

pISSN: 1303-6009

Pooling

deep learning

convolutional neural network

Sayı

Yazarlar:

Yahya Doğan

Yayıncı:

Ankara University

Ana Başlıklar:

Information Systems (Other)

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering Vol.66 Issue 1, pp.95-117

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001304931

pISSN:

1303-6009