According to the comparison with the results of BP network , grey-clustering decision , and single pollution index method , the result of the model is satisfactory . The Hopfield network model in the comprehensive appraisal of water quality has the advantages of simplicity and visibility , and it is easy to be realized . The use of neural networks for integrated lin­guistic analysis may be profitable . This paper presents the first results of our research on that subject : a Hopfield model for syntactical analy­sis . We construct a neural network as an imple­mentation of a bounded push-down automaton , which can accept context-free languages with lim­ited center-embedding . Hopfield Type Network을 응용하여 뉴로 컴퓨팅(Neuro Computing)을 기초로 하는 대학의 시간표에 대한 알고리즘을 연구하였다 . 이 방식에서 시간표 They show the computational advantages and disadvantages of such an approach for different coding schemes and for networks consisting of very simple two-state elements as well as those made up of more complex nodes capable of representing a continuum . It is demonstrated that , with the appropriate hardware , there is a computational advantage in using the Hopfield architecture over some alternative methods for computing the same solution . The relationship between a particular mode of operation of the neural network and the regularized Gerchberg ( 1974 ) and Papoulis ( 1975 ) algorithm is also discussed . > In addition to the two pooling methods , however , there could be many other pooling types , such as geometric average , harmonic average , and so on . Since it is not easy for algorithms to find the best pooling method , usually the pooling types are assumed a priority , which might not be optimal for different tasks . In line with the deep learning philosophy , the type of pooling can be driven by data for a given task . The original quaternion convolution operation is presented and constructed to fully mix the information of color channels . The quaternion batch normalization and pooling operations are derived and designed in quaternion domain to further ensure the integrity of color information . Meanwhile , the knowledge of the attention mechanism is incorporated to boost the performance of the proposed QCNN . In particular , we propose a Nested Invariance Pooling ( NIP ) method to obtain compact and robust Convolutional Neural Network ( CNNs ) descriptors . The CNNs descriptors are generated by applying three different pooling operations to the feature maps of CNNs in a nested way towards rotation and scale invariant feature representation . In particular , the rational , advantages and performance on the combination of CNNs and handcrafted descriptors are provided to better investigate the complementary effects of deep learnt and handcrafted features . In order to improve cost trade-offs , it has recently been suggested to apply pooling designs which enable the detection of carriers of rare alleles in groups of individuals . However , this was shown to hold only for a relatively low number of individuals in a pool , and requires the design of pooling schemes for particular cases . We propose a novel pooling design , based on a compressed sensing approach , which is both general , simple and efficient . HDR-VDP-2 is primarily a visibility prediction metric i.e. whether the signal distortion is visible to the eye and to what extent . Nevertheless , it also employs a pooling function to compute an overall quality score . This paper focuses on the pooling aspect in HDR-VDP-2 and employs a comprehensive database of HDR images ( with their corresponding subjective ratings ) to improve the prediction accuracy of HDR-VDP-2 . Convnets are inherently translation invariant . Their basic components ( convolution , pooling , and activation functions ) operate on local input regions , and depend only on relative spatial coordinates . Writing for the data vector at location in a particular layer , and for the following layer , these functions compute outputs by where is called the kernel size , is the stride or subsampling factor , and determines the layer type : a matrix multiplication for convolution or average pooling , a spatial max for max pooling , or an elementwise nonlinearity for an activation function , and so on for other types of layers . A dynamic pooling layer is applied to aggregate information along different parts of the tree , serving as a way of semantic compositionality . We use the pooling operation , which takes the maximum value in each dimension . Then we add a fully - connected hidden layer to further mix the information in a sentence . If , pooling regions do not overlap ; however , current CNN architectures typically include overlapping pooling with and . Let us now compare the pooling operation defined by Eq . [ reference ] to the standard definition of a convolutional layer applied to feature map given as : where are the convolutional weights ( or the kernel weights , or filters ) , is the activation function , typically a rectified linear activation ReLU , and is the number of output feature ( or channel ) of the convolutional layer . This requirement is “ artificial ” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale . In this work , we equip the networks with another pooling strategy , “ spatial pyramid pooling ” , to eliminate the above requirement . The new network structure , called SPP-net , can generate a fixed-length representation regardless of image size/scale . It is a two stage framework for subspace learning . Firstly , a sparse coding followed by spatial pyramid max pooling is used to get the image representation . Then , the image descriptor is mapped into an orthonormal and smooth subspace to classify images in low dimension . Thus it can be used as a first step in any kind of syntactical analysis . The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images . We performed the experiments on a historical marriage record dataset , obtaining promising results . In this paper , a novel Context-and-Spatial Aware Network ( CSANet ) , which integrates both a Context Aware Path and Spatial Aware Path , is proposed to obtain effective features involving both context information and spatial information . Specifically , we design a Context Aware Path with structure supervision strategy and spatial pyramid pooling strategy to enhance the context information . Meanwhile , a Spatial Aware Path is proposed to preserve the spatial information , which also shortens the information propagation path from low-level features to high-level features . This paper designs a novel image segmentation model to obtain dense feature maps and promote segmentation effects . In encoder stage , we employ ResNet-50 to extract features , and then add a spatial pooling pyramid ( SPP ) to achieve multi-scale feature fusion . In decoder stage , we provide an improved position attention module to integrate contextual information effectively and remove the trivial information through changing the construction way of attention matrix . Unlike previous work that treats spatial pooling and image classification as separated steps , we propose to jointly learn the geometric pooling and image classifier such that class-specific geometric information of local descriptors can be incorporated to improve classification performance . Inspired by previous work of spatial pyramid matching and receptive field learning , we also propose spatial pyramid geometric pooling , receptive field geometric pooling and random partition geometric pooling approaches to further exploit the spatial structural information to boost classification performance . Experiments on 15-scene dataset validate the advantages of our proposed algorithms . Aiming at considering intraclass compactness in HSR-RSIs , we propose a discriminative distance metric learning method with LC ( DDML-LC ) . The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs , and then uses spatial pyramid maximum pooling with sparse coding to encode the features . In the learning process , the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained , leading to a joint optimization of feature manifold , distance metric , and label distribution . Another is a boosted difference feature ( BDF ) that uses a difference operation between a selected pair of features to improve localization accuracy of pedestrian detection . We incorporate a spatial pyramid pool method that uses multiple sized blocks to increase the richness of boosted features in a local region and use a RealBoost method to train a tree-structured classifier for the proposed pedestrian detection method . We also apply a region-of-interest method to the detected results to remove false positives effectively . The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms . We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features . We also show that object information can be transferred through exemplars for detected objects . Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation . For capturing the patch - background context , we show that a network design with traditional multi - scale image input and sliding pyramid pooling is effective for improving performance . Our experimental results set new state - of - the - art performance on a number of popular semantic segmentation datasets , including NYUDv2 , PASCAL VOC 2012 , PASCAL - Context , and SIFT - flow . These feature maps are then concatenated to form one feature map . We perform spatial pyramid pooling ( a modified version using sliding windows ) on the feature map to capture information from background regions in multiple sizes . This increases the field - of - view for the feature map and thus it is able to capture the information from a large image region . We conduct experiments on the PASCAL VOC 2012 segmentation benchmark dataset and use the DeepLab - ASPP - L as the segmentation framework . For each compared method in Table [ reference ] , we replace the convolutional layers in conv4 and conv5 of Table [ reference ] with atrous convolution and plug in a head of Atrous Spatial Pyramid Pooling ( ASPP ) in the final feature maps of conv5 . We adopt the same training strategy for all networks following for fair comparison . Pooling is applied independently to each feature map channel , as in standard max pooling . The RoI layer is simply the special - case of the spatial pyramid pooling layer used in SPPnets [ reference ] in which there is only one pyramid level . We use the pooling sub - window calculation given in [ reference ] . Our method ( section 3 ) starts from an existing network , such as AlexNet pre - trained on ImageNet data , and extends it to reason explicitly and efficiently about image regions R. In order to do so , given an image x , the first step is to efficiently extract region - level descriptors φ ( x ; R ) by inserting a spatial pyramid pooling layer on top of the convolutional layers of the CNN [ reference ][ reference ] . We combine global context in the Fast R - CNN step . Given the full - image conv feature map , we pool a feature by global Spatial Pyramid Pooling [ reference ] ( with a " single - level " pyramid ) which can be implemented as " RoI " pooling using the entire image 's bounding box as the RoI. This pooled feature is fed into the post - RoI layers to obtain a global context feature . This global feature is concatenated with the original per - region feature , followed by the sibling classification and box regression layers . Backbone - Style ” , such as PSPNet , Deeplab v3 . It embeds different scale context information to improve the consistency of network with the Pyramid Spatial Pooling module or Atrous Spatial Pyramid Pooling module . The other one is “ Encoder - Decoder - Style ” , like RefineNet , Global Convolutional Network . Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules named cascade corner pooling and center pooling , which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions , respectively . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , which outperforms all existing one-stage detectors by a large margin .
According to the comparison with the results of BP network , grey-clustering decision , and single pollution index method , the result of the model is satisfactory . The Hopfield network model in the comprehensive appraisal of water quality has the advantages of simplicity and visibility , and it is easy to be realized .
The use of neural networks for integrated lin­guistic analysis may be profitable . This paper presents the first results of our research on that subject : a Hopfield model for syntactical analy­sis . We construct a neural network as an imple­mentation of a bounded push-down automaton , which can accept context-free languages with lim­ited center-embedding .
Hopfield Type Network을 응용하여 뉴로 컴퓨팅(Neuro Computing)을 기초로 하는 대학의 시간표에 대한 알고리즘을 연구하였다 . 이 방식에서 시간표
They show the computational advantages and disadvantages of such an approach for different coding schemes and for networks consisting of very simple two-state elements as well as those made up of more complex nodes capable of representing a continuum . It is demonstrated that , with the appropriate hardware , there is a computational advantage in using the Hopfield architecture over some alternative methods for computing the same solution . The relationship between a particular mode of operation of the neural network and the regularized Gerchberg ( 1974 ) and Papoulis ( 1975 ) algorithm is also discussed . >
In addition to the two pooling methods , however , there could be many other pooling types , such as geometric average , harmonic average , and so on . Since it is not easy for algorithms to find the best pooling method , usually the pooling types are assumed a priority , which might not be optimal for different tasks . In line with the deep learning philosophy , the type of pooling can be driven by data for a given task .
The original quaternion convolution operation is presented and constructed to fully mix the information of color channels . The quaternion batch normalization and pooling operations are derived and designed in quaternion domain to further ensure the integrity of color information . Meanwhile , the knowledge of the attention mechanism is incorporated to boost the performance of the proposed QCNN .
In particular , we propose a Nested Invariance Pooling ( NIP ) method to obtain compact and robust Convolutional Neural Network ( CNNs ) descriptors . The CNNs descriptors are generated by applying three different pooling operations to the feature maps of CNNs in a nested way towards rotation and scale invariant feature representation . In particular , the rational , advantages and performance on the combination of CNNs and handcrafted descriptors are provided to better investigate the complementary effects of deep learnt and handcrafted features .
In order to improve cost trade-offs , it has recently been suggested to apply pooling designs which enable the detection of carriers of rare alleles in groups of individuals . However , this was shown to hold only for a relatively low number of individuals in a pool , and requires the design of pooling schemes for particular cases . We propose a novel pooling design , based on a compressed sensing approach , which is both general , simple and efficient .
HDR-VDP-2 is primarily a visibility prediction metric i.e. whether the signal distortion is visible to the eye and to what extent . Nevertheless , it also employs a pooling function to compute an overall quality score . This paper focuses on the pooling aspect in HDR-VDP-2 and employs a comprehensive database of HDR images ( with their corresponding subjective ratings ) to improve the prediction accuracy of HDR-VDP-2 .
Convnets are inherently translation invariant . Their basic components ( convolution , pooling , and activation functions ) operate on local input regions , and depend only on relative spatial coordinates . Writing for the data vector at location in a particular layer , and for the following layer , these functions compute outputs by where is called the kernel size , is the stride or subsampling factor , and determines the layer type : a matrix multiplication for convolution or average pooling , a spatial max for max pooling , or an elementwise nonlinearity for an activation function , and so on for other types of layers .
A dynamic pooling layer is applied to aggregate information along different parts of the tree , serving as a way of semantic compositionality . We use the pooling operation , which takes the maximum value in each dimension . Then we add a fully - connected hidden layer to further mix the information in a sentence .
If , pooling regions do not overlap ; however , current CNN architectures typically include overlapping pooling with and . Let us now compare the pooling operation defined by Eq . [ reference ] to the standard definition of a convolutional layer applied to feature map given as : where are the convolutional weights ( or the kernel weights , or filters ) , is the activation function , typically a rectified linear activation ReLU , and is the number of output feature ( or channel ) of the convolutional layer .
This requirement is “ artificial ” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale . In this work , we equip the networks with another pooling strategy , “ spatial pyramid pooling ” , to eliminate the above requirement . The new network structure , called SPP-net , can generate a fixed-length representation regardless of image size/scale .
It is a two stage framework for subspace learning . Firstly , a sparse coding followed by spatial pyramid max pooling is used to get the image representation . Then , the image descriptor is mapped into an orthonormal and smooth subspace to classify images in low dimension .
Thus it can be used as a first step in any kind of syntactical analysis . The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images . We performed the experiments on a historical marriage record dataset , obtaining promising results .
In this paper , a novel Context-and-Spatial Aware Network ( CSANet ) , which integrates both a Context Aware Path and Spatial Aware Path , is proposed to obtain effective features involving both context information and spatial information . Specifically , we design a Context Aware Path with structure supervision strategy and spatial pyramid pooling strategy to enhance the context information . Meanwhile , a Spatial Aware Path is proposed to preserve the spatial information , which also shortens the information propagation path from low-level features to high-level features .
This paper designs a novel image segmentation model to obtain dense feature maps and promote segmentation effects . In encoder stage , we employ ResNet-50 to extract features , and then add a spatial pooling pyramid ( SPP ) to achieve multi-scale feature fusion . In decoder stage , we provide an improved position attention module to integrate contextual information effectively and remove the trivial information through changing the construction way of attention matrix .
Unlike previous work that treats spatial pooling and image classification as separated steps , we propose to jointly learn the geometric pooling and image classifier such that class-specific geometric information of local descriptors can be incorporated to improve classification performance . Inspired by previous work of spatial pyramid matching and receptive field learning , we also propose spatial pyramid geometric pooling , receptive field geometric pooling and random partition geometric pooling approaches to further exploit the spatial structural information to boost classification performance . Experiments on 15-scene dataset validate the advantages of our proposed algorithms .
Aiming at considering intraclass compactness in HSR-RSIs , we propose a discriminative distance metric learning method with LC ( DDML-LC ) . The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs , and then uses spatial pyramid maximum pooling with sparse coding to encode the features . In the learning process , the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained , leading to a joint optimization of feature manifold , distance metric , and label distribution .
Another is a boosted difference feature ( BDF ) that uses a difference operation between a selected pair of features to improve localization accuracy of pedestrian detection . We incorporate a spatial pyramid pool method that uses multiple sized blocks to increase the richness of boosted features in a local region and use a RealBoost method to train a tree-structured classifier for the proposed pedestrian detection method . We also apply a region-of-interest method to the detected results to remove false positives effectively .
The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms . We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features . We also show that object information can be transferred through exemplars for detected objects .
Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation . For capturing the patch - background context , we show that a network design with traditional multi - scale image input and sliding pyramid pooling is effective for improving performance . Our experimental results set new state - of - the - art performance on a number of popular semantic segmentation datasets , including NYUDv2 , PASCAL VOC 2012 , PASCAL - Context , and SIFT - flow .
These feature maps are then concatenated to form one feature map . We perform spatial pyramid pooling ( a modified version using sliding windows ) on the feature map to capture information from background regions in multiple sizes . This increases the field - of - view for the feature map and thus it is able to capture the information from a large image region .
We conduct experiments on the PASCAL VOC 2012 segmentation benchmark dataset and use the DeepLab - ASPP - L as the segmentation framework . For each compared method in Table [ reference ] , we replace the convolutional layers in conv4 and conv5 of Table [ reference ] with atrous convolution and plug in a head of Atrous Spatial Pyramid Pooling ( ASPP ) in the final feature maps of conv5 . We adopt the same training strategy for all networks following for fair comparison .
Pooling is applied independently to each feature map channel , as in standard max pooling . The RoI layer is simply the special - case of the spatial pyramid pooling layer used in SPPnets [ reference ] in which there is only one pyramid level . We use the pooling sub - window calculation given in [ reference ] .
Our method ( section 3 ) starts from an existing network , such as AlexNet pre - trained on ImageNet data , and extends it to reason explicitly and efficiently about image regions R. In order to do so , given an image x , the first step is to efficiently extract region - level descriptors φ ( x ; R ) by inserting a spatial pyramid pooling layer on top of the convolutional layers of the CNN [ reference ][ reference ] .
We combine global context in the Fast R - CNN step . Given the full - image conv feature map , we pool a feature by global Spatial Pyramid Pooling [ reference ] ( with a " single - level " pyramid ) which can be implemented as " RoI " pooling using the entire image 's bounding box as the RoI. This pooled feature is fed into the post - RoI layers to obtain a global context feature . This global feature is concatenated with the original per - region feature , followed by the sibling classification and box regression layers .
Backbone - Style ” , such as PSPNet , Deeplab v3 . It embeds different scale context information to improve the consistency of network with the Pyramid Spatial Pooling module or Atrous Spatial Pyramid Pooling module . The other one is “ Encoder - Decoder - Style ” , like RefineNet , Global Convolutional Network .
Our approach , named CenterNet , detects each object as a triplet , rather than a pair , of keypoints , which improves both precision and recall . Accordingly , we design two customized modules named cascade corner pooling and center pooling , which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions , respectively . On the MS-COCO dataset , CenterNet achieves an AP of 47.0 % , which outperforms all existing one-stage detectors by a large margin .
Comprehensive assessment of water quality based on Hopfield network and realization with matlab Hopfield models as neural-network acceptors Hopfield-Type에 만족하는 제약조건 Scheduling 알고리즘 Superresolution algorithms for a modified Hopfield neural network Alpha-Integration Pooling for Convolutional Neural Networks. Quaternion Convolutional Neural Network for Color Image Classification and Forensics Compact Deep Invariant Descriptors for Video Retrieval Rare-Allele Detection Using Compressed Se(que)nsing On improving the pooling in HDR-VDP-2 towards better HDR perceptual quality assessment Fully Convolutional Networks for Semantic Segmentation Natural Language Inference by Tree-Based Convolution and Heuristic Matching Striving for Simplicity: The All Convolutional Net Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Orthogonal and Smooth Subspace Based on Sparse Coding for Image Classification Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling A Context-and-Spatial Aware Network for Multi-Person Pose Estimation Semantic Image Segmentation with Improved Position Attention and Feature Fusion Learning weighted geometric pooling for image classification Learning a Discriminative Distance Metric With Label Consistency for Scene Classification Robust pedestrian detection under deformation using simple boosted features Adaptive region pooling for object detection Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Dual Path Networks Fast R-CNN Weakly Supervised Deep Detection Networks Deep Residual Learning for Image Recognition Learning a Discriminative Feature Network for Semantic Segmentation CenterNet: Object Detection with Keypoint Triplets
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