In order to solve this problem , this paper put forward the on-board GPS filter double localization method . Using activation function controls kalman filter gain and preliminary wea-kens the effect of jitter . Introducing self-adapting attenuation factor establishes a rapid response filter , and combining with the farm machinery motor change track finishes removing jitter influence , completing farm machinery positioning at last . We propose an interpretation of this theory applied to parsing . A construction mechanism is proposed , based on an adapted version of the activation function which takes advantage of the representation of linguistic information in terms of constraints . This feature allows to show how chunks are easy to build and how they can facilitate treatment . In this experiment we fix the depth of network at 3 . Activation Function . Note that we exploit the identity as activation function on neurons of CIN , as shown in Eq . We use a tanh non - linearity as the activation function between layers . Note that for Mult - vae pr , since the output of д ϕ ( · ) is used as the mean and variance of a Gaussian random variable , we do not apply an activation function to it . Thus , the Mult - vae pr with 0 - hiddenlayer mlp is effectively a log - linear model . A non-static activation function is adaptively learned in the training phase of deep network . Furthermore , the proposed activation neuron cooperating with maxout is a potent higher-order function approximator , which can break through the convex curve limitation . Experimental results on object recognition benchmarks demonstrate the effectiveness of the proposed activation scheme . And the weighted cross entropy loss can highlight the area of stroke lesion to enhance the structural contrast . We also introduce other techniques in our network , like GN [ 13 ] , Maxout [ 14 ] in the proposed network . Data augmentation is also used in the training phase . This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions . With the help of recently-developed network embedding feature generation methods and deep maxout neural networks , it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function . Our novel method , STRING2GO , successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information . With the help of recently-developed network embedding feature generation methods and deep maxout neural networks , it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function . Our novel method , STRING2GO , successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information . The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition . Its operation is closely related to convolutional networks , as it applies a similar pooling step , but over different neurons evaluated on the same input . Here , we combine the two technologies , and experiment with deep convolutional neural networks built from maxout neurons . Phone recognition tests on the TIMIT database show that switching to maxout units from rectifier units decreases the phone error rate for each network configuration studied , and yields relative error rate reductions of between 2 % and 6 % . The extraction of handwritten characters from document images , e.g. printed text with handwritten annotation , plays important roles in the field of the document analysis and recognition . In this paper , the maxout filters that are composed with the maxout activation functions are trained and applied to extraction of the handwritten characters from the documents . The maxout filter for the extraction is an extension of the morphological closing filter , which can eliminate the valleys of intensity surface of images . For extraction of the handwritten characters , the parameters of the maxout filters are trained to eliminate only the printed characters , which appear as the valleys of the intensity . In order to handle multiple writers , a batch learning that trains parameters with averaging the errors of each writer is introduced . Therefore , this paper develops a facial expression recognition algorithm based on the deep learning method . An adaptive model parameter initialization based on the multilayer maxout network linear activation function is proposed to initialize the convolutional neural network ( CNN ) and the long – short-term memory network ( LSTM ) method . It can effectively overcome the gradient disappearance and gradient explosion problems in the deep learning model training process . Recently , the machine learning community has proposed new methods to improve the accuracy of DNNs by using techniques such as dropout and maxout . In this paper , we investigate Deep Maxout Networks DMN for acoustic modeling in a noisy automatic speech recognition environment . Experiments show that DMNs improve substantially the recognition accuracy over DNNs and other traditional techniques in both clean and noisy conditions on the TIMIT dataset . In this paper , we devise an efficient anchor-based cascade framework called anchor cascade . To improve the detection accuracy by exploring contextual information , we further propose a context pyramid maxout mechanism for anchor cascade . As a result , anchor cascade can train very efficient face detection models with a high detection accuracy . Arabic is widely articulated by Malay race due to several factors such as ; performing worship and reciting the Holy book of Muslims . Newly , Maxout deep neural networks have conveyed substantial perfections to speech recognition systems . Hence , in this paper , a fully connected feed-forward neural network with Maxout units is introduced . Instead of pre-segmenting the image to layers , the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module . The essential components of the soft-mask module are maxout and fuse operations , which enable a disjoint layered representation of optical flow and more accurate flow estimation . We show that by using masks the motion estimate results in a quadratic function of input features in the output layer . This study investigates the effectiveness of multiple maxout activation function variants on 18 datasets using Convolutional Neural Networks . A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions . Stacking these modules forms the GoogLeNet architecture . employ a variant with maxout in place of concatenation . Figure [ reference ] makes apparent our connection with such work . We observe all state - of - the - art results on MNIST , CIFAR - 10 , CIFAR - 100 , and SVHN . It is also worth mentioning that our formulation is inclusive to various techniques proposed recently such as averaging , dropconnect , and Maxout . We expect to see more classification error reduction with careful engineering for DSN . Max pooling Spectral pooling ( a ) Approximation loss for the ImageNet validation set . Method CIFAR - 10 CIFAR - 100 Stochastic pooling 15.13 % 41.51 % Maxout 11.68 % 38.57 % Network - in - network 10.41 % 35.68 % Deeply supervised 9.78 % 34.57 % Spectral pooling 8.6 % 31.6 % ( b ) Classification rates . [ 20 ] show that on difficult classification instances , the network corrects itself by emphasising and de - emphasising certain filters , outperforming a previous state - of - the - art CNN . 2 Maxout Networks In this work we use the Maxout networks [ 7 ] , combined with dropout [ 21 ] , as the underlying model for dasNet . Maxout networks represent the state - of - the - art for object recognition in various tasks and have only been outperformed ( by a small margin ) by averaging committees of several convolutional neural networks . ϕ ( xi ∗ F ℓi , j ) , ( 5 ) where aℓj is the weight of the j - th output map in layer ℓ , changing the strength of its activation , before applying the maxout pooling operator . The vector a = After a rapid first shot image classification through a standard stack of feedforward filters , the feedback can actively alter the importance of certain filters “ in hindsight ” , correcting the initial guess via additional internal “ thoughts ” . DasNet successfully learned to correct image misclassifications produced by a fully trained feedforward Maxout network . Its active , selective , internal spotlight of attention enabled state - of - the - art results . LSUV outperforms orthonormal initialization and both LSUV and orthonormal outperform Hints initialization . The error rates of the Deeply - Supervised Nets ( DSN , ) and maxout networks , the current state - of - art , are provided for reference . Since the widely cited DSN error rate of 0.39 % , the state - of - the - art ( until recently ) was obtained after replacing the softmax classifier with SVM , we do the same and also observe improved results ( line FitNet - LSUV - SVM in Table [ reference ] ) . in Eq . ( [ reference ] ) had a hidden layer of 64 maxout units . The initial states of both the encoder and generator were treated as additional parameters . ( [ reference ] ) is a hyperbolic tangent function . The computation from the hidden state in the decoder to the output is implemented as a deep neural network with a single intermediate layer having 500 maxout units each pooling 2 inputs . All the weight parameters in the RNN Encoder – Decoder were initialized by sampling from an isotropic zero - mean ( white ) Gaussian distribution with its standard deviation fixed to , except for the recurrent weight parameters . In the above , all results are obtained by single - scale training / testing as in [ reference ] , where the image 's shorter side is s = 600 pixels . Multi - scale training / testing has been developed in [ reference ][ reference ] by selecting a scale from a feature pyramid , and in [ reference ] by using maxout layers . In our current implementation , we have performed multi - scale testing following [ reference ] ; we have not performed multi - scale training because of limited time . There are multiple choices for this non - linearity , such as the sigmoid , hyperbolic tangent and rectified linear functions , . Recently , proposed a Maxout non - linearity , which has been shown to be particularly effective at modeling useful features . Maxout features are associated with multiple kernels . At each step of the decoder , we compute the output probability ( Eq . ( [ reference ] ) ) as a multi - layered function . We use a single hidden layer of maxout units and normalize the output probabilities ( one for each word ) with a softmax function ( see Eq . ( [ reference ] ) ) . subsection : Recurrent nets with maxout activations We remind the reader that maxout nets generalize ReLu units by employing non - linearities of the form where the subsets of neurons are typically disjoint . In we have shown that maxout DNNs and CNNs trained with annealed dropout outperform their sigmoid - based counterparts on both 300 hours and 2000 hours training regimes . To alleviate the potential problems due the zero gradient , Leaky ReLU ( LReLU ) was proposed . Recently , a parametrized form of ReLU ( PReLU ) was shown to give superior performance compared to ReLU on large scale computer vision tasks . PReLU is a generalized version of LReLU where the gradient is learned adaptively from the training data . In this paper we investigate PReLU based deep convolutional neural networks for noise robust speech recognition . We report experimental results on Aurora-4 multi-condition training task . We show that PReLU gives slightly better Word Error Rates ( WERs ) on noisy test sets compared to ReLU . In combination with dropout generalization method we report one of the best WERs in the literature for this noisy speech recognition task . Aiming at the shortcomings of traditional medical image recognition methods , this paper proposes an adaptive convolutional neural network model CNN-BN-PReLU based on the convolutional neural network method . The model first performs batch normalization ( BN ) processing on the input of each feature map of each layer of network , and then adaptively adjusts the parameters by using Parametric Rectified Linear Unit ( PReLU ) to compare the BN algorithm . Based on the performance before and after the activation function , an adaptive convolutional neural network model is constructed . To avoid the tedious and almost impossible task of finding a target orientation to learn , we propose to use Siamese networks which implicitly find the optimal orientations during training . We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU , maxout , and PReLU activation functions . This novel activation performs better for our task . Theorem of Multiplication of n variables by a NN with n-th P , and m-th P enclosure of PReLU are provided . PReLU NN is compared with the polynomial implying the enclosure . Learnings of the approximation by the shallow , or the deep PReLU NNs are also discussed . Through extensive experiments and analysis , we observed that the performance of binary networks is sensitive to activation distribution variations . Based on this important observation , we propose to generalize the traditional Sign and PReLU functions , denoted as RSign and RPReLU for the respective generalized functions , to enable explicit learning of the distribution reshape and shift at near-zero extra cost . Lastly , we adopt a distributional loss to further enforce the binary network to learn similar output distributions as those of a real-valued network . Aiming at the problems of huge parameters and network degradation caused by simple linear stacked convolution layers or continuous full connection layers in traditional expression recognition methods , two convolution neural network models are designed through depth separation convolution and residual module respectively to widen and deepen the network . Firstly , model A adopts depth separation convolution instead of regular convolution layer , and the global average pooling layer replaces the final full connection layer , utilizes the methods of dropout , batch normalization , activation function of PReLU and image augmentation to avoid over-fitting effectively . Model B adopts pre-trained ResNet50 model to extract facial features , magnifies the images twice by the SRGAN method . Multiple hidden layers have been used in the architecture . Dropout , Normalization and Parametric Rectified Linear Unit ( PReLU ) technology has been used to generalize and improve the accuracy of model . Also , the impact of various hyper-parameter has analysed . In CoGAN , the generative models are based on the fractional length convolutional ( FCONV ) layers , while the discriminative models are based on the standard convolutional ( CONV ) layers with the exceptions that the last two layers are based on the fully - connected ( FC ) layers . The batch normalization ( BN ) layers are applied after each convolutional layer , which are followed by the parameterized rectified linear unit ( PReLU ) processing . The sigmoid units and the hyperbolic tangent units are applied to the output layers of the generative models for generating images with desired pixel range values . In this paper , we develop a novel mini - batch aware regularization where only parameters of non - zero features appearing in each mini - batch participate in the calculation of L2 - norm , making the computation acceptable . Besides , we design a data adaptive activation function , which generalizes commonly used PReLU by adaptively adjusting the rectified point w.r.t . distribution of inputs and is shown to be helpful for training industrial networks with sparse features . The contributions of this paper are summarized as follows : We point out the limit of using fixed - length vector to express user ’s diverse interests and design a novel deep interest network ( DIN ) which introduces a local activation unit to adaptively learn the representation of user interests from historical behaviors w.r.t . We tuned the number of layers , layer dimensionality and dropout rates to optimize performance on the development set , using the Adam optimizer . Batch normalization ioffe2015batch and PReLU units he2015delving were used . This indicates that the degradation problem is well addressed in this setting and we manage to obtain accuracy gains from increased depth . Second , compared to its plain counterpart , the 34 - layer 6.8 PReLU - net [ reference ] 4.94 BN - inception [ reference ] 4.82 ResNet ( ILSVRC [ reference ] 3.57 Table 5 . Error rates ( % ) of ensembles . Indeed , the number of output feature maps is times larger in the QCNN due to the quaternion convolution , meaning quaternion - valued feature maps correspond to real - valued ones . The PReLU activation function is employed for both models . A dropout of and a regularization of are used across all the layers , except the input and output ones . Rectified linear activation units are important components for state-of-the-art deep convolutional networks . In this paper , we propose a novel S-shaped rectified linear activation unit ( SReLU ) to learn both convex and non-convex functions , imitating the multiple function forms given by the two fundamental laws , namely the Webner-Fechner law and the Stevens law , in psychophysics and neural sciences . Specifically , SReLU consists of three piecewise linear functions , which are formulated by four learnable parameters . The SReLU is learned jointly with the training of the whole deep network through back propagation . During the training phase , to initialize SReLU in different layers , we propose a " freezing " method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values . SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost . Experiments with two popular CNN architectures , Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10 , CIFAR100 , MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions . This results in several spatial-frequency domain switchings that are computationally very costly , and as such , it would be advantageous to strictly stay in the frequency domain . Hence , in this work , a novel Spectral Rectified Linear Unit ( SReLU ) for the activation function is proposed , that makes it possible for the computations to remain in the frequency domain , and therefore avoids the multiple compute-intensive domain transformations . To further optimize the classification speed of the network , an efficient spectral-based CNN model is presented that uses only the lower frequency components by way of fusing the convolutional and sub-sampling layers . Batch normalization improved ReLU and LReLU networks , but did not improve ELU and SReLU networks ( see Fig . [ reference ] ) . Besides , six pooling techniques were compared : average pooling , max pooling , stochastic pooling , rank-based average pooling , rank-based weighted pooling , and rank-based stochastic pooling . Results The results over 100 test set showed the combination of parametric ReLU and rank-based stochastic pooling performed the best , with sensitivity of 93.4 % , specificity of 94.6 % , precision of 94.5 % , and accuracy of 94.0 % . This result is better than six state-of-the-art breast cancer detection approaches . To alleviate the potential problems due the zero gradient , Leaky ReLU ( LReLU ) was proposed . Recently , a parametrized form of ReLU ( PReLU ) was shown to give superior performance compared to ReLU on large scale computer vision tasks . PReLU is a generalized version of LReLU where the gradient is learned adaptively from the training data . We introduce the “ exponential linear unit ” ( ELU ) which speeds up learning in deep neural networks and leads to higher classification accuracies . Like rectified linear units ( ReLUs ) , leaky ReLUs ( LReLUs ) and parametrized ReLUs ( PReLUs ) , ELUs alleviate the vanishing gradient problem via the identity for positive values . However ELUs have improved learning characteristics compared to the units with other activation functions .
In order to solve this problem , this paper put forward the on-board GPS filter double localization method . Using activation function controls kalman filter gain and preliminary wea-kens the effect of jitter . Introducing self-adapting attenuation factor establishes a rapid response filter , and combining with the farm machinery motor change track finishes removing jitter influence , completing farm machinery positioning at last .
We propose an interpretation of this theory applied to parsing . A construction mechanism is proposed , based on an adapted version of the activation function which takes advantage of the representation of linguistic information in terms of constraints . This feature allows to show how chunks are easy to build and how they can facilitate treatment .
In this experiment we fix the depth of network at 3 . Activation Function . Note that we exploit the identity as activation function on neurons of CIN , as shown in Eq .
We use a tanh non - linearity as the activation function between layers . Note that for Mult - vae pr , since the output of д ϕ ( · ) is used as the mean and variance of a Gaussian random variable , we do not apply an activation function to it . Thus , the Mult - vae pr with 0 - hiddenlayer mlp is effectively a log - linear model .
A non-static activation function is adaptively learned in the training phase of deep network . Furthermore , the proposed activation neuron cooperating with maxout is a potent higher-order function approximator , which can break through the convex curve limitation . Experimental results on object recognition benchmarks demonstrate the effectiveness of the proposed activation scheme .
And the weighted cross entropy loss can highlight the area of stroke lesion to enhance the structural contrast . We also introduce other techniques in our network , like GN [ 13 ] , Maxout [ 14 ] in the proposed network . Data augmentation is also used in the training phase .
This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions . With the help of recently-developed network embedding feature generation methods and deep maxout neural networks , it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function . Our novel method , STRING2GO , successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information .
With the help of recently-developed network embedding feature generation methods and deep maxout neural networks , it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function . Our novel method , STRING2GO , successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information . The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition .
Its operation is closely related to convolutional networks , as it applies a similar pooling step , but over different neurons evaluated on the same input . Here , we combine the two technologies , and experiment with deep convolutional neural networks built from maxout neurons . Phone recognition tests on the TIMIT database show that switching to maxout units from rectifier units decreases the phone error rate for each network configuration studied , and yields relative error rate reductions of between 2 % and 6 % .
The extraction of handwritten characters from document images , e.g. printed text with handwritten annotation , plays important roles in the field of the document analysis and recognition . In this paper , the maxout filters that are composed with the maxout activation functions are trained and applied to extraction of the handwritten characters from the documents . The maxout filter for the extraction is an extension of the morphological closing filter , which can eliminate the valleys of intensity surface of images . For extraction of the handwritten characters , the parameters of the maxout filters are trained to eliminate only the printed characters , which appear as the valleys of the intensity . In order to handle multiple writers , a batch learning that trains parameters with averaging the errors of each writer is introduced .
Therefore , this paper develops a facial expression recognition algorithm based on the deep learning method . An adaptive model parameter initialization based on the multilayer maxout network linear activation function is proposed to initialize the convolutional neural network ( CNN ) and the long – short-term memory network ( LSTM ) method . It can effectively overcome the gradient disappearance and gradient explosion problems in the deep learning model training process .
Recently , the machine learning community has proposed new methods to improve the accuracy of DNNs by using techniques such as dropout and maxout . In this paper , we investigate Deep Maxout Networks DMN for acoustic modeling in a noisy automatic speech recognition environment . Experiments show that DMNs improve substantially the recognition accuracy over DNNs and other traditional techniques in both clean and noisy conditions on the TIMIT dataset .
In this paper , we devise an efficient anchor-based cascade framework called anchor cascade . To improve the detection accuracy by exploring contextual information , we further propose a context pyramid maxout mechanism for anchor cascade . As a result , anchor cascade can train very efficient face detection models with a high detection accuracy .
Arabic is widely articulated by Malay race due to several factors such as ; performing worship and reciting the Holy book of Muslims . Newly , Maxout deep neural networks have conveyed substantial perfections to speech recognition systems . Hence , in this paper , a fully connected feed-forward neural network with Maxout units is introduced .
Instead of pre-segmenting the image to layers , the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module . The essential components of the soft-mask module are maxout and fuse operations , which enable a disjoint layered representation of optical flow and more accurate flow estimation . We show that by using masks the motion estimate results in a quadratic function of input features in the output layer .
This study investigates the effectiveness of multiple maxout activation function variants on 18 datasets using Convolutional Neural Networks . A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions .
Stacking these modules forms the GoogLeNet architecture . employ a variant with maxout in place of concatenation . Figure [ reference ] makes apparent our connection with such work .
We observe all state - of - the - art results on MNIST , CIFAR - 10 , CIFAR - 100 , and SVHN . It is also worth mentioning that our formulation is inclusive to various techniques proposed recently such as averaging , dropconnect , and Maxout . We expect to see more classification error reduction with careful engineering for DSN .
Max pooling Spectral pooling ( a ) Approximation loss for the ImageNet validation set . Method CIFAR - 10 CIFAR - 100 Stochastic pooling 15.13 % 41.51 % Maxout 11.68 % 38.57 % Network - in - network 10.41 % 35.68 % Deeply supervised 9.78 % 34.57 % Spectral pooling 8.6 % 31.6 % ( b ) Classification rates .
[ 20 ] show that on difficult classification instances , the network corrects itself by emphasising and de - emphasising certain filters , outperforming a previous state - of - the - art CNN . 2 Maxout Networks In this work we use the Maxout networks [ 7 ] , combined with dropout [ 21 ] , as the underlying model for dasNet . Maxout networks represent the state - of - the - art for object recognition in various tasks and have only been outperformed ( by a small margin ) by averaging committees of several convolutional neural networks .
ϕ ( xi ∗ F ℓi , j ) , ( 5 ) where aℓj is the weight of the j - th output map in layer ℓ , changing the strength of its activation , before applying the maxout pooling operator . The vector a =
After a rapid first shot image classification through a standard stack of feedforward filters , the feedback can actively alter the importance of certain filters “ in hindsight ” , correcting the initial guess via additional internal “ thoughts ” . DasNet successfully learned to correct image misclassifications produced by a fully trained feedforward Maxout network . Its active , selective , internal spotlight of attention enabled state - of - the - art results .
LSUV outperforms orthonormal initialization and both LSUV and orthonormal outperform Hints initialization . The error rates of the Deeply - Supervised Nets ( DSN , ) and maxout networks , the current state - of - art , are provided for reference . Since the widely cited DSN error rate of 0.39 % , the state - of - the - art ( until recently ) was obtained after replacing the softmax classifier with SVM , we do the same and also observe improved results ( line FitNet - LSUV - SVM in Table [ reference ] ) .
in Eq . ( [ reference ] ) had a hidden layer of 64 maxout units . The initial states of both the encoder and generator were treated as additional parameters .
( [ reference ] ) is a hyperbolic tangent function . The computation from the hidden state in the decoder to the output is implemented as a deep neural network with a single intermediate layer having 500 maxout units each pooling 2 inputs . All the weight parameters in the RNN Encoder – Decoder were initialized by sampling from an isotropic zero - mean ( white ) Gaussian distribution with its standard deviation fixed to , except for the recurrent weight parameters .
In the above , all results are obtained by single - scale training / testing as in [ reference ] , where the image 's shorter side is s = 600 pixels . Multi - scale training / testing has been developed in [ reference ][ reference ] by selecting a scale from a feature pyramid , and in [ reference ] by using maxout layers . In our current implementation , we have performed multi - scale testing following [ reference ] ; we have not performed multi - scale training because of limited time .
There are multiple choices for this non - linearity , such as the sigmoid , hyperbolic tangent and rectified linear functions , . Recently , proposed a Maxout non - linearity , which has been shown to be particularly effective at modeling useful features . Maxout features are associated with multiple kernels .
At each step of the decoder , we compute the output probability ( Eq . ( [ reference ] ) ) as a multi - layered function . We use a single hidden layer of maxout units and normalize the output probabilities ( one for each word ) with a softmax function ( see Eq . ( [ reference ] ) ) .
subsection : Recurrent nets with maxout activations We remind the reader that maxout nets generalize ReLu units by employing non - linearities of the form where the subsets of neurons are typically disjoint . In we have shown that maxout DNNs and CNNs trained with annealed dropout outperform their sigmoid - based counterparts on both 300 hours and 2000 hours training regimes .
To alleviate the potential problems due the zero gradient , Leaky ReLU ( LReLU ) was proposed . Recently , a parametrized form of ReLU ( PReLU ) was shown to give superior performance compared to ReLU on large scale computer vision tasks . PReLU is a generalized version of LReLU where the gradient is learned adaptively from the training data . In this paper we investigate PReLU based deep convolutional neural networks for noise robust speech recognition . We report experimental results on Aurora-4 multi-condition training task . We show that PReLU gives slightly better Word Error Rates ( WERs ) on noisy test sets compared to ReLU . In combination with dropout generalization method we report one of the best WERs in the literature for this noisy speech recognition task .
Aiming at the shortcomings of traditional medical image recognition methods , this paper proposes an adaptive convolutional neural network model CNN-BN-PReLU based on the convolutional neural network method . The model first performs batch normalization ( BN ) processing on the input of each feature map of each layer of network , and then adaptively adjusts the parameters by using Parametric Rectified Linear Unit ( PReLU ) to compare the BN algorithm . Based on the performance before and after the activation function , an adaptive convolutional neural network model is constructed .
To avoid the tedious and almost impossible task of finding a target orientation to learn , we propose to use Siamese networks which implicitly find the optimal orientations during training . We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU , maxout , and PReLU activation functions . This novel activation performs better for our task .
Theorem of Multiplication of n variables by a NN with n-th P , and m-th P enclosure of PReLU are provided . PReLU NN is compared with the polynomial implying the enclosure . Learnings of the approximation by the shallow , or the deep PReLU NNs are also discussed .
Through extensive experiments and analysis , we observed that the performance of binary networks is sensitive to activation distribution variations . Based on this important observation , we propose to generalize the traditional Sign and PReLU functions , denoted as RSign and RPReLU for the respective generalized functions , to enable explicit learning of the distribution reshape and shift at near-zero extra cost . Lastly , we adopt a distributional loss to further enforce the binary network to learn similar output distributions as those of a real-valued network .
Aiming at the problems of huge parameters and network degradation caused by simple linear stacked convolution layers or continuous full connection layers in traditional expression recognition methods , two convolution neural network models are designed through depth separation convolution and residual module respectively to widen and deepen the network . Firstly , model A adopts depth separation convolution instead of regular convolution layer , and the global average pooling layer replaces the final full connection layer , utilizes the methods of dropout , batch normalization , activation function of PReLU and image augmentation to avoid over-fitting effectively . Model B adopts pre-trained ResNet50 model to extract facial features , magnifies the images twice by the SRGAN method .
Multiple hidden layers have been used in the architecture . Dropout , Normalization and Parametric Rectified Linear Unit ( PReLU ) technology has been used to generalize and improve the accuracy of model . Also , the impact of various hyper-parameter has analysed .
In CoGAN , the generative models are based on the fractional length convolutional ( FCONV ) layers , while the discriminative models are based on the standard convolutional ( CONV ) layers with the exceptions that the last two layers are based on the fully - connected ( FC ) layers . The batch normalization ( BN ) layers are applied after each convolutional layer , which are followed by the parameterized rectified linear unit ( PReLU ) processing . The sigmoid units and the hyperbolic tangent units are applied to the output layers of the generative models for generating images with desired pixel range values .
In this paper , we develop a novel mini - batch aware regularization where only parameters of non - zero features appearing in each mini - batch participate in the calculation of L2 - norm , making the computation acceptable . Besides , we design a data adaptive activation function , which generalizes commonly used PReLU by adaptively adjusting the rectified point w.r.t . distribution of inputs and is shown to be helpful for training industrial networks with sparse features . The contributions of this paper are summarized as follows : We point out the limit of using fixed - length vector to express user ’s diverse interests and design a novel deep interest network ( DIN ) which introduces a local activation unit to adaptively learn the representation of user interests from historical behaviors w.r.t .
We tuned the number of layers , layer dimensionality and dropout rates to optimize performance on the development set , using the Adam optimizer . Batch normalization ioffe2015batch and PReLU units he2015delving were used .
This indicates that the degradation problem is well addressed in this setting and we manage to obtain accuracy gains from increased depth . Second , compared to its plain counterpart , the 34 - layer 6.8 PReLU - net [ reference ] 4.94 BN - inception [ reference ] 4.82 ResNet ( ILSVRC [ reference ] 3.57 Table 5 . Error rates ( % ) of ensembles .
Indeed , the number of output feature maps is times larger in the QCNN due to the quaternion convolution , meaning quaternion - valued feature maps correspond to real - valued ones . The PReLU activation function is employed for both models . A dropout of and a regularization of are used across all the layers , except the input and output ones .
Rectified linear activation units are important components for state-of-the-art deep convolutional networks . In this paper , we propose a novel S-shaped rectified linear activation unit ( SReLU ) to learn both convex and non-convex functions , imitating the multiple function forms given by the two fundamental laws , namely the Webner-Fechner law and the Stevens law , in psychophysics and neural sciences . Specifically , SReLU consists of three piecewise linear functions , which are formulated by four learnable parameters . The SReLU is learned jointly with the training of the whole deep network through back propagation . During the training phase , to initialize SReLU in different layers , we propose a " freezing " method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values . SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost . Experiments with two popular CNN architectures , Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10 , CIFAR100 , MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions .
This results in several spatial-frequency domain switchings that are computationally very costly , and as such , it would be advantageous to strictly stay in the frequency domain . Hence , in this work , a novel Spectral Rectified Linear Unit ( SReLU ) for the activation function is proposed , that makes it possible for the computations to remain in the frequency domain , and therefore avoids the multiple compute-intensive domain transformations . To further optimize the classification speed of the network , an efficient spectral-based CNN model is presented that uses only the lower frequency components by way of fusing the convolutional and sub-sampling layers .
Batch normalization improved ReLU and LReLU networks , but did not improve ELU and SReLU networks ( see Fig . [ reference ] ) .
Besides , six pooling techniques were compared : average pooling , max pooling , stochastic pooling , rank-based average pooling , rank-based weighted pooling , and rank-based stochastic pooling . Results The results over 100 test set showed the combination of parametric ReLU and rank-based stochastic pooling performed the best , with sensitivity of 93.4 % , specificity of 94.6 % , precision of 94.5 % , and accuracy of 94.0 % . This result is better than six state-of-the-art breast cancer detection approaches .
To alleviate the potential problems due the zero gradient , Leaky ReLU ( LReLU ) was proposed . Recently , a parametrized form of ReLU ( PReLU ) was shown to give superior performance compared to ReLU on large scale computer vision tasks . PReLU is a generalized version of LReLU where the gradient is learned adaptively from the training data .
We introduce the “ exponential linear unit ” ( ELU ) which speeds up learning in deep neural networks and leads to higher classification accuracies . Like rectified linear units ( ReLUs ) , leaky ReLUs ( LReLUs ) and parametrized ReLUs ( PReLUs ) , ELUs alleviate the vanishing gradient problem via the identity for positive values . However ELUs have improved learning characteristics compared to the units with other activation functions .
Farm Machinery on-board GPS Location Accuracy Simulation and Research Chunks and the notion of activation : a facilitation model for sentence processing (Chunks et activation : un modèle de facilitation du traitement linguistique) [in French] xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Variational Autoencoders for Collaborative Filtering Deep networks with non-static activation function Stroke Lesion Segmentation with 2D Novel CNN Pipeline and Novel Loss Function Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks Convolutional deep maxout networks for phone recognition An Extraction Method of Handwritten Characters on Printed Documents by Maxout Filter Networks Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM Deep Maxout Networks Applied to Noise-Robust Speech Recognition Anchor Cascade for Efficient Face Detection Maxout based deep neural networks for Arabic phonemes recognition Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask Evaluation of maxout activations in deep learning across several big data domains FractalNet: Ultra-Deep Neural Networks without Residuals Deeply-Supervised Nets Spectral Representations for Convolutional Neural Networks Deep Networks with Internal Selective Attention through Feedback Connections All you need is a good init Attention-Based Models for Speech Recognition Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Deep Residual Learning for Image Recognition Brain tumor segmentation with Deep Neural Networks Neural Machine Translation by Jointly Learning to Align and Translate The IBM 2016 English Conversational Telephone Speech Recognition System Investigation of parametric rectified linear units for noise robust speech recognition Brain Image Recognition Algorithm and High Performance Computing of Internet of Medical Things Based on Convolutional Neural Network Learning to Assign Orientations to Feature Points A Multiplication by a Neural Network (NN) with Power Activations and a Polynomial Enclosure for a NN with PReLUs ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions Face expression recognition based on improved convolutional neural network Sentiment Analysis On Movie Reviews Using Recurrent Neural Network Coupled Generative Adversarial Networks Deep Interest Network for Click-Through Rate Prediction Order-Embeddings of Images and Language Deep Residual Learning for Image Recognition Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Deep Learning with S-Shaped Rectified Linear Activation Units Spectral-based convolutional neural network without multiple spatial-frequency domain switchings Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling Investigation of parametric rectified linear units for noise robust speech recognition Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
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TALN (1) KDD WWW BrainLes@MICCAI (1) INTERSPEECH ISPACS IberSPEECH ICLR AISTATS NIPS NIPS ICLR NIPS EMNLP 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Image Anal. ICLR INTERSPEECH INTERSPEECH CVPR MWSCAS AIPR NIPS KDD ICLR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) INTERSPEECH ICLR INTERSPEECH ICLR
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