1. bookTom 15 (2022): Zeszyt 1 (January 2022)
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Fast fourier transform based new pooling layer for deep learning

Data publikacji: 16 Apr 2022
Tom & Zeszyt: Tom 15 (2022) - Zeszyt 1 (January 2022)
Zakres stron: 1 - 14
Otrzymano: 01 Jul 2021
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1178-5608
Pierwsze wydanie
01 Jan 2008
Częstotliwość wydawania
1 raz w roku
Języki
Angielski
Introduction

Convolutional neural networks (CNN) have become growing area of interest in last years, because it can be used in different application such as image segmentation (Farabet et al., 2013), image classification (Krizhevsky and Hinton, 2017), object detection (Ren et al., 2017), image capturing (Vinyals et al., 2015), face recognition and more other applications. One of the most important layers of this network is convolution layer. It is considered one of the fundamental component of building blocks of a CNN network due to best extraction of features, which is required for most of application. It is used for extracting features from an input data and then feeding it to the next layer through the network, but the most disadvantage of this layer is that it is produces huge volume of data at its output based on input size, the number of filters and number of used channels, therefore high cost for computation and complexity is required, which may decrease efficiency of the model and therefore decrease the accuracy of model performance. Different methods are used to solve this problem, the most important method of them is using pooling layer to reduce the dimension of the data of convolution or down sampling the size of convolution layer output. The parameters of pooling layer are the filter size and strides values. In order to reduce the data to its one quarter of original side, signal processing methods can be used to process the data by using different techniques such as using threshold method (Kaushik et al., 2019), or applying filter along the signal, the most commonly used pooling size is with filter size 2 and with stride 2. Two most famous types of pooling layers are Max. pooling (Saeedan et al., 2018) and average pooling (Lee et al., 2016), in the Max. method, the maximum value is selected to be the feature map for the pooling size window and average value is taken in average pooling method. Max pooling is used more likely than average pooling. Although, these methods are effective and simple, but they may produce omission of significant information and dilation of important details of the data. Other pooling method are proposed such as mixed and stochastic method, which used the probabilistic calculation to correct some weakness of the previous standard deterministic methods (Farabet et al., 2013). In this paper, A novel pooling method is proposed by using discrete Fourier transform (DFT), this method is used DFT technique to transform the data from spatial domain into frequency domain to preserve the most important information from the details coefficients, where the details information of the image is less significant, therefore it can be discarded to down sample the size of dimensions. Its effect will be great with advantage of reducing the eliminated details information as compared with other standard methods. After applying DFT, the most significant coefficients are cropped while less important details will be discarded then the data is reconstructed by applying inverse DFT, Different methods are proposed based on the scenario of using DFT and select the best coefficients for reconstructing the data (Yaroslavsky, 2014; Taigman et al., 2017).

Literature survey

Xiaym et al. used DCT to compress the image data, which reduced the amount of data through the intermediate networks of hidden layer. The work is accomplished by using small set of most important DCT coefficients rather than all pixels of image. These coefficients are used as input into two layer auto encoder instead. They showed that combining of DCT with deep learning increase model accuracy for image classification by exploiting the DCT compaction properties. The auto encoder was used to generate another a new vector, which reconstructed by decoder, then based on selection of best vector obtained by minimum values of error between original and reconstructed vector, thus, the auto encoder will achieve best result depending on best extracted features (Xioayi et al., 2014).

Xu and Hideki in 2019 was introduced a pooling method based on DCT technique, which is used to process output data of convolution layer. DCT was used to isolate the basic frequencies to isolate the features of input signal based on DCT compaction property. It can extract most significant information. They showed that the method is better than exiting works based on the obtained results with negligible time of consumption (Xu et al., 2019).

(Ulicny et al., 2020) used CNN model to learn the features, which are extracted from DCT coefficients. First, DCT was applied to compress the data as in image compression, based on compressing the harmonic networks, which have less information by discarding high frequency coefficients or other redundancy. They showed that, their work can compress the data with best performance (Ulicny et al., 2020).

Tavis William and Roberli is proposed pooling layer by using discrete wavelet transform (DWT), this method was used decomposed sub bands of the second level of wavelet transform as approximation coefficients which are used with (details coefficients is replaced by zero value) to reconstruct the data, based on the principle of approximation coefficients provides the approximation shape of the original data. Then best features of the original data are extracted by discarding less important information (Williams and Li, 2016).

Zeiler and Fegus proposed pooling method by elite activation, which selected from distribution multidimensional by activation pool size region of pooling. It is accomplished by evaluating probability function of each region, the selected probability will be normalized. The pooling features map will be sample from the distribution depending on determined probability. Different methods were used to elite the probabilities. The elited elements to be pooling feature map may be not the greater value (Zeiler and Fergus, 2013).

Kobayashi used distribution probabilities over activation for extract the feature data based on determining the standard deviation and mean statistics of distribution for Gaussian function for each pooling window. The basic idea work is to concise and summarize distribution of Gaussian function and aggregate activation function into average and standard deviation. This method is applied in stochastic pooling method, which used to determine the mapped features of the data (Kobayashi, 2019).

Hamad and Jasim are proposed a new pooling layer for CNN classification model by using Gaussian density function for determining wavelet transform coefficients. These coefficients are used as filters weights for wavelet transform to get the approximation subband, which used later by different methods to evaluate the mapped features (Hamad alhussainy and Jasim, 2020).

In this paper, A novel pooling method is proposed by using DFT, this method is used DFT technique to transform the data from spatial domain into frequency domain to preserve the most important information from the details coefficients, where the details information of the image is less significant, therefore it can be discarded to down sample the size of dimensions without discarding significant information from the data. Its effect will be great with advantage of reducing the eliminated details information as compared with other standard methods, which may result in high reduction in the information, therefore this method can overcome most disadvantage of the previous method. This pooling method discarded less information based on isolating less significant component of the data by using fourier transform, while most other methods pool the features by down sample the data in spatial domain, which may produce high loss of data.

Proposed methodology

In this paper, A novel methods layer are proposed based on DFT named as FTM. DFT is isolate the frequency coefficients according its significant, then most important coefficients are cropped to be features of convolution layer output, therefore the amount of discarded information will be reduced and this can overcome disadvantage of standard Max. method or average method, which eliminated discarding of some basic details information of the data. The proposed methods are explained in the block diagram in Figure 1.

Figure 1:

Proposed pooling layer.

The original data (which always is output of convolution layer) are applied to DFT, then shifting and cropping pre-processing are perform to select the most important frequencies of the data, which are used to map the original data while less significant frequencies which is represented low important details are eliminated, the cropping of frequencies are performed based on the required stride of pool size, then inverse DFT is used to reconstruct the down sampled data, which will be input for upper layer as shown the figure. In the feedback, the gradient error is determined in reverse manner for the pooling procedure, first the DFT is applied to transform the data into frequency domain, then padding is used to resample the data to its original size according to the information from feed forward operation and the basic parameters, which are saved at cache, then inverse of DFT is applied to reconstruct the data to be input for lower layer as shown in Figure 1.

Discrete Fourier transform (DFT)

DFT can be applied to image to provide more details description about characteristics of the image and it is determined by Eq. (1) (Yaroslavsky, 2014). F ( u , v ) = 1 N M " x = 1 y = 1 M - 1 N - 1 f ( x , y ) [ cos ( 2 π ( ux M + vy M ) ) ] - j [ sin ( 2 π ( ux M + vy M ) ) ] , (1)where u  =  0,1,…..M – 1, also v = 0,1,2…..N – 1. As shown in the equation above the transform have real and imaginary part. The inverse transform is determined by Eq. (2). f ( X , y ) = 1 N M x = 1 y = 1 M - 1 N - 1 F ( u , v ) [ cos ( 2 π ( ux M + vy M ) ) ] - j [ sin ( 2 π ( ux M + vy M ) ) ]

Proposed algorithms

Three pooling methods are proposed by using DFT named as FTM1, FTM2 and FTM3. DFT are used to isolate the different frequency components of the data, then by selecting most important frequencies of DFT transform, which represents most significant features of the data, then IDFT is performed to reconstruct original data. Different methods are proposed based on the procedure of processing of frequency components of the transform, these algorithms are explained in the following sections.

Algorithm 1 (FTM1)

This algorithm is extract features map by applying DFT, which is used to transform the output of convolution layer to frequency components, then shift operation is performed to select most significant coefficients for reconstructing the original data by inverse DFT. The details of this algorithm is explained in Figure 2.

Figure 2:

Proposed first (FTM1) algorithm.

Algorithm 2 (FTM2)

In this algorithm, features map are extracted by applying DFT, which is used to transform the output of convolution layer to frequency components, then different filters (low pass and high pass filters) are used compress the coefficients into small set, then most significant coefficients are cropped to be used for reconstructing the original data by inverse DFT. The details is explained in Figure 3.

Figure 3:

Proposed second (FTM2) algorithm.

Algorithm 3 (FTM3)

This algorithm is constructed from mixing of the two previous algorithms by applying the two above methods in parallel manner, then the pooling data will be mixed of result of these methods based on determined maximum element for each pooling window to be used as mixing parameter. The description of this method is shown in Figure 4.

Figure 4:

Proposed third (FTM3) algorithm.

Experimental results and discussions

The performance of the proposed methods were evaluated by several tests, including first, the proposed methods were applied to the standard images, and the pooled images were extracted from them, then the original images were retrieved using only the pooled images. Then the retrieved images are compared with original images by using different measures such as SNR, correlation and SSIM to determine the similarity between original and reconstructed image. Also, the proposed pooling methods were used as pooling layers in CNN model for image classification, and it was tested by using two types of databases, which are (MNIST and CIFAR_10), and the results were compared with the results of standard methods (Average pooling and Max. pooling layers). The experiments were implemented using matlab2019a windows10, with CPU 2.7GHz, Intel cori_7, 2400 and RAM 16GB.

Results of test images

The proposed algorithms are applied to standard images to extract best feature to be mapped image with summarized dimensions size. The performance efficiency of the pooling method is evaluated by comparing the reconstructed image after pooling with the original image. The results are determined based on similarity measurements for standard “Lena”, “cameraman” and “Barbara” images. The original images size was (256*256), then these images were mapped to size of (128*128) as shown in Table 1, which showed development of metrics values for SSIM, correlation and SNR values. (FTM1) method achieved (24.1754), (20.29) and (29.38) in SNR for “Lena”, cameraman and Barbara images, which are good improvement compared with (23.28 dB, 20.14 dB and 23.69 dB) for Saeedan et al. (2018) method and (24.01 dB, 21.87 dB and 22.13 dB) for Lee et al. (2016) pooling method. Also in terms of correlation, the proposed method (FTM1) were achieved (0.9846), (0.9952) and (0.9612) verses (0.9822), (0.9814) and (0.9622) for Saeedan et al. (2018) method for “Lena”, cameraman and Barbara images, respectively. Therefore, the pooled features represent most significant characteristics of original image. The details of the results are shown in Table 1, while quality of the extracted pooling image using (FTM1) are shown in Figure 5. It is clear that the reconstructed image look like the original image due to best extraction of features with discarding of less important data through pooling map.

Performance of proposed methods.

Images Metrics Saeedan et al. (2018) Lee et al. (2016) FTM1 FTM2 FTM3
Lena SNR dB 23.28 24.01 24.1754 24.1603 24.16785
SSIM 0.7882 0.7893 0.79396 0.77886 0.78641
Correlation 0.9822 0.9833 0.9846 0.9695 0.97705
Cameraman SNR 20.14 21.87 20.2935 20.2784 20.28595
SSIM 0.7854 0.7867 0.7891 0.774 0.78155
Correlation 0.9814 0.9843 0.9952 0.9801 0.98765
Barbara SNR 23.69 22.13 27.336 27.3209 27.32845
SSIM 0.7041 0.7065 0.7092 0.6941 0.70165
Correlation 0.9622 0.9648 0.9612 0.9461 0.95365
Test image SNR 28.98 28.78 29.38 29.3649 29.37245
SSIM 0.7832 0.7841 0.7852 0.7701 0.77765
Correlation 0.9913 0.9922 0.9965 0.9814 0.98895

Figure 5:

Original images, pooled images and reconstructed images by FTM1.

Results of image classification

The proposed pooling methods were used as pooling layer in CNN model for image classification. This model was tested by using two types of databases, which are (MNIST and CIFAR_10).

Results of MNIST classification

The results of MNIST classification by using the proposed pooling methods are shown in Table 2. It is clear that the proposed methods achieved higher accuracy than standard methods (Average and Max.) methods. The proposed method satisfied (Acc. 99.96%), which obtained by reduction of discarding significant frequencies details component of the image. Figure 6 shows comparison between the satisfied accuracy by the proposed methods verses the results of standard methods while sensitivity results is shown in Figure 7 and precision comparison for the proposed methods is described in Figure 8.

Proposed methods results for MNIST dataset.

Method Saeedan et al. (2018) Lee et al. (2016) FTM1 FTM2 FTM3
Accuracy (%) 98.80 98.72 99.95 99.84 99.96

Figure 6:

Accuracy of MNIST classification for proposed method.

Figure 7:

Sensitivity results for proposed method for MNIST classification.

Figure 8:

Specificity results for proposed method for MNIST classification.

The training of the model steps for (FTM1) method is shown in Figure 9. The accuracy is upgraded to greater than (97% accuracy) in 2 epochs iteration only, also the loss function is dropped to less than 0.21, which prove that the proposed pooling method is extracted best characteristics of the input data. The confusion matrix description of the results for matching is listed in Table 3.

Figure 9:

Training steps for MNIST classification by (FTM1) method.

Confusion matrix for (FTM1) method for MNIST Classification.

Target class
Class 0 Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9 Sensitivity
Output class
 Class 0 250 0 0 0 0 0 0 0 0 0 100
 Class 1 0 250 0 0 0 0 0 0 0 0 100
10% %
 Class 2 0 0 250 0 0 0 0 0 0 0 100
10% %
 Class 3 0 0 0 250 0 0 0 0 0 0 100
10% %
 Class 4 0 0 0 0 250 0 0 0 0 0 100
10% %
 Class 5 0 0 0 0 0 250 0 0 0 0 100
10% %
 Class 6 0 0 0 0 0 0 249 0 0 0 100
10% %
 Class 7 0 0 0 0 0 0 0 250 0 0 100
10% %
 Class 8 0 0 0 0 0 0 1 0 250 0 99.9
10% 6%
 Class 9 0 0 0 0 0 0 0 0 0 250 100
10% %
100 100 100 100 100 100 100 99.9 100 100 100
% % % % % % % 6% % % %
CIFAR_10 dataset results

The proposed methods also used for classification of second dataset, which is CIFAR10 with size of (60000*32*32*3), where 3 represents (RGB) image. The classification model trained using 50,000 images, while other 1,000 images used to test the system efficiency. The model has been trained up to 10 epochs by 390 iteration per epoch with batch size 128 and the initial learning rate (0.01). The obtained results of the proposed methods were compared with results of (Max. method and Average method), which are shown in Table 4. The proposed methods achieved better results compared to the results of the standard methods, as their accuracy rate were (73.88), (73.82), (73.76) (FTM1, FTM 2 and FTM 3) methods, respectively, while it were (72.59%), (72.4%) for the standard (Saeedan et al., 2018; Lee et al., 2016) methods, respectively. Superior classification results were achieved with FTM1 method due to its best isolation of important frequency and therefore eliminate less important frequencies, depending on the shift process of frequencies which leads to the loss of the least possible information from the original data and recovery of the best characteristics of the information. The performance descriptions are shown in Figures 10, 11 and 12, for accuracy, specificity and precision respectively. The matching between classification results and actual results are shown in Table 5. As shown in this table, there is high matching between the predicted and actual classes, also the it achieved high rate of sensitivity and specificity for all classes, which prove that this method can be applied for different types of images.

Accuracy results for CIFAR10 dataset classification.

Method Saeedan et al. (2018) Lee et al. (2016) RFT1 RFT2 RFT3
Accuracy (%) 72.59 72.4 73.88 73.82 73.76

Figure 10:

Accuracy results for proposed method for CIFAR 10 classification.

Figure 11:

Specificity results for proposed method for CIFAR 10 classification.

Figure 12:

Precision results for proposed method for CIFAR 10 classification.

Confusion matrix for (FTM1) for CIFAR_10 classification.

Target class
airplane auto mobi bird cat deer dog frog hour ship truc Sens
Output class
 airpla 796 29 68 32 25 13 10 22 57 3 73
 autom 10 829 6 6 4 2 3 4 15 7 86
 bird 44 10 599 62 69 42 37 32 13 8 66
 cat 18 3 61 533 36 16 57 43 10 9 56
 deer 24 3 75 60 708 38 26 55 5 1 71
 dog 4 4 109 201 55 69 22 97 4 3 58
 frog 9 9 46 66 54 13 83 5 7 1 78
 hours 7 1 16 12 38 25 3 72 2 4 86
 ship 48 36 9 14 9 6 4 6 86 2 82
 truck 38 75 8 13 4 6 2 8 18 80 83
 specifi 79.8 82.3 59.8 53.3 71.6 69 83 73 86 82 74
Conclusions

Convolution layer considered the most important layer in deep learning due to its ability to extract basic features of the data through the network, therefore it is used in most D.L models such as CNN model, but because this layer is used more number of filters and different numbers of channels, the size of the data will be increased, which may need to huge volume of computation and lead to reduce the model efficiency. Thus, different methods are used to solve this problem, one of them is using pooling layer. Also pooling layer may discard important information from the data, therefore best features of data must be selected to reduce the loosing of information. In this paper, A new methods layer are proposed based on DFT. DFT isolated the frequency coefficients according to its significant, then most important coefficients are cropped to be features of convolution layer output, therefore the amount of discarded information will be reduced and this can overcome most disadvantage of standard Max. method or average method, which were eliminated some of basic details information of the data, therefore, this pooling method discarded less information based on isolating less significant component of the data by using Fourier transform, while most other methods pool the features by down sample the data in spatial domain, which may produce high loss of data.

The proposed methods were applied to the standard images, and the pooled images were extracted from them, then the original images were retrieved using only the pooled images. Then the retrieved images are compared with original images by using different measures such as MSE, PSNR and SSIM to determine the similarity between original and reconstructed image. (FTM1) achieved (32.868 dB), (24.19 dB) and (26.58 dB) in SNR for “Lena”, cameraman and Barbara images, verses (24.28 dB, 23.14 dB and 21.69 dB), respectively, for [Saeedan et al. (2018) method] and (24.01 dB, 22.87 dB and 22.13dB), respectively, for [Lee et al. (2016)] pooling method. The proposed layers used for image classification by using two different datasets, which are MNIST and CIFAR_10. Specifications results of classification are evaluated using different metrics like accuracy, specificity and precision. The classification of MNIST achieved (Acc: 99.96%) more than (Saeedan et al. (2018) 98.8%), (Lee et al. (2016) 98.72%). For classification of CIFAR_10, the proposed method satisfied (73.88%) better than (Saeedan et al. (2018) method 72.59%) and (Lee et al. (2016) method 72.4%). The results proved that the proposed methods outperformed standard methods, thus it can be used for deep learning application. The most limitation of this method is that it required more calculation cost due to more computation operation, which may lead to increase required time for training, therefore batch normalization layer may be used in future depending on the third dimension of the data to reduce the execution time Figures 6 and 7.

Figure 1:

Proposed pooling layer.
Proposed pooling layer.

Figure 2:

Proposed first (FTM1) algorithm.
Proposed first (FTM1) algorithm.

Figure 3:

Proposed second (FTM2) algorithm.
Proposed second (FTM2) algorithm.

Figure 4:

Proposed third (FTM3) algorithm.
Proposed third (FTM3) algorithm.

Figure 5:

Original images, pooled images and reconstructed images by FTM1.
Original images, pooled images and reconstructed images by FTM1.

Figure 6:

Accuracy of MNIST classification for proposed method.
Accuracy of MNIST classification for proposed method.

Figure 7:

Sensitivity results for proposed method for MNIST classification.
Sensitivity results for proposed method for MNIST classification.

Figure 8:

Specificity results for proposed method for MNIST classification.
Specificity results for proposed method for MNIST classification.

Figure 9:

Training steps for MNIST classification by (FTM1) method.
Training steps for MNIST classification by (FTM1) method.

Figure 10:

Accuracy results for proposed method for CIFAR 10 classification.
Accuracy results for proposed method for CIFAR 10 classification.

Figure 11:

Specificity results for proposed method for CIFAR 10 classification.
Specificity results for proposed method for CIFAR 10 classification.

Figure 12:

Precision results for proposed method for CIFAR 10 classification.
Precision results for proposed method for CIFAR 10 classification.

Proposed methods results for MNIST dataset.

Method Saeedan et al. (2018) Lee et al. (2016) FTM1 FTM2 FTM3
Accuracy (%) 98.80 98.72 99.95 99.84 99.96

Confusion matrix for (FTM1) for CIFAR.10 classification.

Target class
airplane auto mobi bird cat deer dog frog hour ship truc Sens
Output class
 airpla 796 29 68 32 25 13 10 22 57 3 73
 autom 10 829 6 6 4 2 3 4 15 7 86
 bird 44 10 599 62 69 42 37 32 13 8 66
 cat 18 3 61 533 36 16 57 43 10 9 56
 deer 24 3 75 60 708 38 26 55 5 1 71
 dog 4 4 109 201 55 69 22 97 4 3 58
 frog 9 9 46 66 54 13 83 5 7 1 78
 hours 7 1 16 12 38 25 3 72 2 4 86
 ship 48 36 9 14 9 6 4 6 86 2 82
 truck 38 75 8 13 4 6 2 8 18 80 83
 specifi 79.8 82.3 59.8 53.3 71.6 69 83 73 86 82 74

Accuracy results for CIFAR10 dataset classification.

Method Saeedan et al. (2018) Lee et al. (2016) RFT1 RFT2 RFT3
Accuracy (%) 72.59 72.4 73.88 73.82 73.76

Performance of proposed methods.

Images Metrics Saeedan et al. (2018) Lee et al. (2016) FTM1 FTM2 FTM3
Lena SNR dB 23.28 24.01 24.1754 24.1603 24.16785
SSIM 0.7882 0.7893 0.79396 0.77886 0.78641
Correlation 0.9822 0.9833 0.9846 0.9695 0.97705
Cameraman SNR 20.14 21.87 20.2935 20.2784 20.28595
SSIM 0.7854 0.7867 0.7891 0.774 0.78155
Correlation 0.9814 0.9843 0.9952 0.9801 0.98765
Barbara SNR 23.69 22.13 27.336 27.3209 27.32845
SSIM 0.7041 0.7065 0.7092 0.6941 0.70165
Correlation 0.9622 0.9648 0.9612 0.9461 0.95365
Test image SNR 28.98 28.78 29.38 29.3649 29.37245
SSIM 0.7832 0.7841 0.7852 0.7701 0.77765
Correlation 0.9913 0.9922 0.9965 0.9814 0.98895

Confusion matrix for (FTM1) method for MNIST Classification.

Target class
Class 0 Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9 Sensitivity
Output class
 Class 0 250 0 0 0 0 0 0 0 0 0 100
 Class 1 0 250 0 0 0 0 0 0 0 0 100
10% %
 Class 2 0 0 250 0 0 0 0 0 0 0 100
10% %
 Class 3 0 0 0 250 0 0 0 0 0 0 100
10% %
 Class 4 0 0 0 0 250 0 0 0 0 0 100
10% %
 Class 5 0 0 0 0 0 250 0 0 0 0 100
10% %
 Class 6 0 0 0 0 0 0 249 0 0 0 100
10% %
 Class 7 0 0 0 0 0 0 0 250 0 0 100
10% %
 Class 8 0 0 0 0 0 0 1 0 250 0 99.9
10% 6%
 Class 9 0 0 0 0 0 0 0 0 0 250 100
10% %
100 100 100 100 100 100 100 99.9 100 100 100
% % % % % % % 6% % % %

Farabet, C., Couprie, C., Najman, L. and LeCun, Y. 2013. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8): 1915–1929. Farabet C. Couprie C. Najman L. LeCun Y. 2013 Learning hierarchical features for scene labeling IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8): 1915 1929 10.1109/TPAMI.2012.23123787344 Search in Google Scholar

Hamad alhussainy, A. M. and Jasim, A. D. 2020. A novel pooling layer based on gaussian function with wavelet transform. Indonesian Journal of Electrical Engineering and Computer Science 20(3): 1289–1298. Hamad alhussainy A. M. Jasim A. D. 2020 A novel pooling layer based on gaussian function with wavelet transform Indonesian Journal of Electrical Engineering and Computer Science 20(3): 1289 1298 10.11591/ijeecs.v20.i3.pp1289-1298 Search in Google Scholar

Kaushik, M., Gupta, S. H. and Balyan, V. 2019. Evaluating threshold distance by using eigen values and analyzing its impact on the performance of WBAN. 2019 6th International Conference on Signal Processing and Integrated. Kaushik M. Gupta S. H. Balyan V. 2019 Evaluating threshold distance by using eigen values and analyzing its impact on the performance of WBAN. 2019 6th International Conference on Signal Processing and Integrated 10.1109/SPIN.2019.8711666 Search in Google Scholar

Kobayashi, T.2019. Global feature guided local pooling. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 3365–3374. Kobayashi T. 2019 Global feature guided local pooling. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South) pp. 3365 3374 Search in Google Scholar

Krizhevsky, A. and Hinton, G. E. 2017. Using very deep autoencoders for content-based image retrieval. ESANN, Belgium, pp. 1–7. Krizhevsky A. Hinton G. E. 2017 Using very deep autoencoders for content-based image retrieval ESANN Belgium pp. 1 7 Search in Google Scholar

Lee, C.-Y., Gallagher, P. W. and Tu, Z. 2016. Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree. In AISTATS 51(17): 464–472. Lee C.-Y. Gallagher P. W. Tu Z. 2016 Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree In AISTATS 51 (17) 464 472 Search in Google Scholar

Ren, S., He, K., Girshick, R. and Sun, J. 2017. Faster RCNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6): 1137–1149. Ren S. He K. Girshick R. Sun J. 2017 Faster RCNN: towards real-time object detection with region proposal networks IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6): 1137 1149 10.1109/TPAMI.2016.257703127295650 Search in Google Scholar

Saeedan, F., Weber, N., Goesele, M. and Roth, S.2018. Detail-preserving pooling in deep networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 9108–9116. Saeedan F. Weber N. Goesele M. Roth S. 2018 Detail-preserving pooling in deep networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT pp. 9108 9116 Search in Google Scholar

Taigman, Y., Yang, M., Ranzato, M. and Wolf, L. 2017. DeepFace: closing the gap to human-level performance in face verification. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 1701–1708. Taigman Y. Yang M. Ranzato M. Wolf L. 2017 DeepFace: closing the gap to human-level performance in face verification. IEEE Conference on Computer Vision and Pattern Recognition, Columbus pp. 1701 1708 Search in Google Scholar

Ulicny, M., Krylov, V. A. and Dahyot, R. 2020. Harmonic convolutional networks based on discrete cosine transform. arXiv:2001.06570v1. Ulicny M. Krylov V. A. Dahyot R. 2020 Harmonic convolutional networks based on discrete cosine transform arXiv:2001.06570v1 Search in Google Scholar

Vinyals, O., Toshev, A., Bengio, S. and Erhan, D. 2015. Show and tell: a neural image caption generator. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. Vinyals O. Toshev A. Bengio S. Erhan D. 2015 Show and tell: a neural image caption generator. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3156 3164 Search in Google Scholar

Williams, T. and Li, R. 2016. Advanced image classification using wavelets and convolutional neural networks. 15th IEEE International Conference on Machine Learning and Applications, pp. 233–239. Williams T. Li R 2016 Advanced image classification using wavelets and convolutional neural networks. 15th IEEE International Conference on Machine Learning and Applications pp. 233 239 Search in Google Scholar

Xioayi, Z., Xiangmin, X., Chunmei, Q. and Xiaofen, X. 2014. High speed deep networks based on discrete cosine transformation. Image Processing (ICIP), 2014 IEEE International Conference on image processing, pp. 5921–5925. Xioayi Z. Xiangmin X. Chunmei Q. Xiaofen X. 2014 High speed deep networks based on discrete cosine transformation. Image Processing (ICIP), 2014 IEEE International Conference on image processing pp. 5921 5925 Search in Google Scholar

Xu, Y. and Hideki, N.2019. DCT Based Information-Preserving Pooling for Deep Neural Networks. (ICIP), Taipei, Taiwan. Xu Y. Hideki N. 2019 DCT Based Information-Preserving Pooling for Deep Neural Networks. (ICIP), Taipei Taiwan 10.1109/ICIP.2019.8802962 Search in Google Scholar

Yaroslavsky, L. P. 2014. Fast Transforms in Image Processing: Compression, Restoration, and Resampling, Hindawi Publishing Corporation Advances in Electrical Engineering Volume. Yaroslavsky L. P. 2014 Fast Transforms in Image Processing: Compression, Restoration, and Resampling, Hindawi Publishing Corporation Advances in Electrical Engineering Volume 10.1155/2014/276241 Search in Google Scholar

Zeiler, M. and Fergus, R. 2013. Stochastic pooling for regularization of deep convolutional neural Networks. In ICLR. Zeiler M. Fergus R. 2013 Stochastic pooling for regularization of deep convolutional neural Networks. In ICLR Search in Google Scholar

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