In conclusion , we discuss the question of possible accuracy decrease and the application of proposed transformation to different recognition problems . convolutional neural networks , computational optimization , separable filters , complexity reduction . It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment . First , we show that such learned perceptual similarity metrics ( LPIPS ) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment . In arbitrarily constructed simulation environments , we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling . Besides , through analysis of receptive fields of feature representations , deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes . The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning . This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet . This is a valuable contribution because in general convolutional networks are not scale invariant . Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images . A key challenge in generic object detection is being to handle large variations in object scale , poses , viewpoints , especially part deformations when determining the location for specified object categories . Recent advances in deep neural networks have achieved promising results for object detection by extending the traditional detection methodologies using the convolutional neural network architectures . In this paper , we make an attempt to incorporate another traditional detection schema , Regionlet into an end-to-end trained deep learning framework , and perform ablation studies on its behavior on multiple object detection datasets . The HLS framework can significantly boost the efficiency of existing convolution networks for both image classification task and object detection task without increasing network parameters and computational complexity . Extensive experimental results on different vision tasks demonstrate consistent improvement can be achieved across a variety of datasets ( e.g. , CIFAR-10/100 , ImageNet-2012 , PASCAL VOC and MS-COCO ) and different convolutional neural network architectures . Denoising auto - encoders can be stacked to form a deep network by feeding output of the previous layer to the current layer as input . Jain and Seung proposed to use Convolutional Neural Networks ( CNN ) to denoise natural images . Their framework is the same as the recent Fully Convolutional Neural Networks ( FCN ) for semantic image segmentation and other tasks such as super - resolution , although their network is not as deep as today ’s models . subsection : Token Representation We follow the bi - directional LSTM - CNN architecture , where convolutional neural networks ( CNNs ) encode characters inside a word into character - level representation then concatenated with its word - level into context - independent representation . To further enhance the word representation , we leverage an external representation from pretrained ELMo ( Embeddings from Language Models ) layers according to ELMo ELMo . A single QRNN layer thus performs an input - dependent pooling , followed by a gated linear combination of convolutional features . As with convolutional neural networks , two or more QRNN layers should be stacked to create a model with the capacity to approximate more complex functions . subsection : Variants Face recognition performance using deep learning has seen dramatic improvements in recent years . Convolutional networks trained with large datasets of millions of images of thousands of subjects have shown remarkable capability of learning facial representations that are invariant to age , pose , illumination and expression ( A - PIE ) . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset . A large body of work based on neural networks for text similarity tasks including NLI has been published in recent years . The dominating trend in these models is to build complex , deep text representation models , for example , with convolutional networks or long short - term memory networks with the goal of deeper sentence comprehension . While these approaches have yielded impressive results , they are often computationally very expensive , and result in models having millions of parameters ( excluding embeddings ) . Specifically , the dual predictions consist of a forward prediction to generate the next word from the current input word , as well as a backward prediction to reconstruct the input word using the predicted word . DPN has two appealing properties : 1 ) By introducing an extra supervision signal on the prediction , DPN can better capture the interplay between the input and the target ; 2 ) Utilizing the reconstructed input , DPN can make another new prediction . During the test phase , we average both predictions to formulate the final target sentence . These rules are applied in distributed perception networks ( DPN ) , a multi-agent systems approach to distributed Bayesian information fusion . While each DPN agent has limited fusion capabilities , multiple DPN agents can autonomously collaborate to form complex modular fusion systems . Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations . These rules are applied in distributed perception networks ( DPN ) , a multi agent systems approach to distributed Bayesian information fusion . While each DPN agent has limited fusion capabilities , multiple DPN agents can autonomously collaborate to form complex modular fusion systems . Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations . Finally we developed a model transformation method that translates a set of synchronous guarded actions – an intermediate format for synchronous systems – to an asynchronous actor description language ( CAL ) . Our theorem ensures that one passed the correctness verification , the generated DPN of asynchronous pro- cesses ( or actors ) preserves the functional behavior of the original synchronous network . Moreover , by the correctness of the synchronous network , our theorem guarantees that the derived DPN is deadlock-free and can be implemented with only finitely bounded buffers . Finally , we employed a framework for feature learning and classification with the deep polynomial network ( DPN ) on dual-modal features to distinguish between malignant and benign breast tumors . With the leave-one-out cross validation , the DPN method on dual-modal features achieved a sensitivity of 97.8 % , a specificity of 94.1 % , an accuracy of 95.6 % , a Youden 's index of 91.9 % and an area under the receiver operating characteristic curve of 0.961 , which was superior to the classic single-modal methods , and the dual-modal methods using the principal component analysis and multiple kernel learning . These results have demonstrated that the dual-modal AI-based technique with DPN has the potential for breast tumor classification in future clinical practice . Next , we present an analysis of the requirements for successful introduction of these tools into network operations which is based on exploiting the synergies of multiple technologies , the functional and architectural integra- tion of the tools within the users environments , and the adherence to a software life cycle model which ensures the quality and evolution of the developed tools . Finally , the paper presents a detailed case study which shows the successful application of the above principles to the development of advanced surveillance tools for the DPN packet switching networks . We have developed a new two-way time transfer modem , which adopts a dual pseudo-random noise ( DPN ) code . The usage of DPN signal dramatically improves time transfer precision without increasing connection fees of commercial communication satellites . DPN affords concise models that can be conveniently developed and easily transformed into discrete event simulation models . With the help of a simple FMS example , which includes a number of part types , loading rules , dispatching rules , and probabilistic branching ( at an inspection station ) , we illustrate the DPN model development . As an illustration of the ease with which it can be tranformed into a simulation model , we have developed a generalized simulator called ROBSIM and outline here its methodological basis . In this work , we present a simple , highly efficient and modularized Dual Path Network ( DPN ) for image classification which presents a new topology of connection paths internally . By revealing the equivalence of the state-of-the-art Residual Network ( ResNet ) and Densely Convolutional Network ( DenseNet ) within the HORNN framework , we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations . The decision plan net ( DPN ) or decision tree ( DT ) is a graphic representation of preferences and the way they are traded off when supply is low . The method allows one to determine compensatory and non-compensatory choice rules that people use . ( d ) with 26 % smaller model size , 25 % less computational cost and 8 % lower memory consumption , and a deeper DPN ( DPN - 131 ) further pushes the state - of - the - art single model performance with about 2 times faster training speed . Experiments on the Places365 large - scale scene dataset , PASCAL VOC detection dataset , and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet , ResNet and the latest ResNeXt model over various applications . Table [ reference ] shows the results for different models . The DPN - 92 costs about fewer parameters than ResNeXt - 101 ( d ) , while the DPN - 98 costs about fewer parameters than ResNeXt - 101 ( d ) . A Dual-Path Model . In this study , we investigated the effect of personal recognition on the proactive customer service performance . Methods We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously . At first , a novel dual-path module is used to enhance the expressiveness of cardiac image representation . Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins . To this end , we present Hyper-Pairing Network ( HPN ) , a 3D fully convolution neural network which effectively integrates information from different phases . The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange . Additionally , a pairing loss is added to encourage the commonality between high-level feature representations of different phases . By revealing the equivalence of the state-of-the-art Residual Network ( ResNet ) and Densely Convolutional Network ( DenseNet ) within the HORNN framework , we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations . To enjoy the benefits from both path topologies , our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures . Extensive experiments on three benchmark datasets , ImagNet-1k , Places365 and PASCAL VOC , clearly demonstrate superior performance of the proposed DPN over state-of-the-arts . The analysis results along with simulation performed via SMPL show that the proposed scheme is able to enhance the deadline meet ratio of messages by overcoming the network errors . Using the proposed error control scheme , the hard real-time network can be built at cost lower than , but performacne comparable to the dual link network . We show that a coherent dual-link network can be analyzed as though it is a centralized system . We then discuss practical considerations in implementing a dual-link network , and implications of this work to address problems observed in the IEEE 802.6 metropolitan area network standard . Based on the dual problem formulation , the recurrent neural network called the dual routing network has much simpler architecture than the one based on the primal problem formulation . The dynamics and architecture of the dual routing network are defined and the operating characteristics are demonstrated via simulation . The invention discloses a method of releasing a radio bearer in a dual-connection network and a system , which relates to the technical field of radio communication . The problem of releasing a DRB ( Data Radio Bearer ) between a SeNB and UE in the dual-connection network by adopting a 3C frame can be solved . In certain trigger conditions , a MeNB can initiate a DRB releasing process between the SeNB and the UE , the realization method is simple , advantages of the dual-connection technology are used thoroughly , and normal service transmission of the user can be ensured . The invention relates to a dual-network routing method . The method comprises the following steps : nodes are initialized to establish routing registry connection and generate a routing list ; a first communication link is adopted for transmitting data , and the first communication link is preferentially switched to a second communication link to continue to transmit data in the case of communication link interrupt . Under certain assumptions about the cost structure and the optimality of the subproblem solutions , the performance guarantees for both the heuristic and linear programming relaxation of the combined overlay optimization model are 33 % . We also develop heuristic and linear programming performance guarantees for specialized models , including a dual path connectivity model with a worst-case performance guarantee of 25 % , and an uncapacitated multicommodity network design model with a worst-case performance guarantee ( approximately ) proportional to the square root of the number of commodities . They are turned off when not in use to save power . We modify the operation of a pipeline stage to accommodate the dual-path scheme by using time-interleaving capacitor sets . Operating at 200 MS/s sampling rate , this ADC consumes 5.37 mW from a 1 V supply . The frequency-separated property is analyzed , and it shows the capability to design the filtering function separately in each path . Moreover , the dual-path topology is compatible with both symmetric and asymmetric dual-band characteristic . Two microstrip implementations are provided to demonstrate the reliability of dual-path dual-band topology . document : Dual Path Networks In this work , we present a simple , highly efficient and modularized Dual Path Network ( DPN ) for image classification which presents a new topology of connection paths internally . Although the width of the densely connected path increases linearly as it goes deeper , causing the number of parameters to grow quadratically , DenseNet provides higher parameter efficiency compared with the ResNet . In this work , we aim to study the advantages and limitations of both topologies and further enrich the path design by proposing a dual path architecture . In particular , we first provide a new understanding of the densely connected networks from the lens of a higher order recurrent neural network ( HORNN ) , and explore the relations between densely connected networks and residual networks . In this work , we aim to provide a deeper understanding of the densely connected network , from the lens of Higher Order RNN , and explain how the residual networks are in indeed a special case of densely connected network . Based on these analysis , we then propose a novel Dual Path Network architecture that not only achieves higher accuracy , but also enjoys high parameter and computational efficiency . section : Revisiting ResNet , DenseNet and Higher Order RNN However , different may extract the same type of features multiple times , leading to high redundancy . In the following section , we present the dual path networks which can overcome both inherent limitations of these two state - of - the - art network architectures . Their relations with HORNN also imply that our proposed architecture can be used for improving HORNN , which we leave for future works . In the following , we first introduce and formulate the dual path architecture , and then present the network structure in details with complexity analysis . subsection : Dual Path Architecture Sec . Recently , the equivalence to RNNs has been further extended to DenseNet . Based on this finding , Dual Path Networks were proposed and showed superior performance to DenseNet and ResNet in a varity of applications . section : Single - State Recurrent Networks
In conclusion , we discuss the question of possible accuracy decrease and the application of proposed transformation to different recognition problems . convolutional neural networks , computational optimization , separable filters , complexity reduction .
It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment . First , we show that such learned perceptual similarity metrics ( LPIPS ) are susceptible to adversarial attacks that dramatically contradict human visual similarity judgment .
In arbitrarily constructed simulation environments , we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling . Besides , through analysis of receptive fields of feature representations , deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes . The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning .
This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet . This is a valuable contribution because in general convolutional networks are not scale invariant . Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images .
A key challenge in generic object detection is being to handle large variations in object scale , poses , viewpoints , especially part deformations when determining the location for specified object categories . Recent advances in deep neural networks have achieved promising results for object detection by extending the traditional detection methodologies using the convolutional neural network architectures . In this paper , we make an attempt to incorporate another traditional detection schema , Regionlet into an end-to-end trained deep learning framework , and perform ablation studies on its behavior on multiple object detection datasets .
The HLS framework can significantly boost the efficiency of existing convolution networks for both image classification task and object detection task without increasing network parameters and computational complexity . Extensive experimental results on different vision tasks demonstrate consistent improvement can be achieved across a variety of datasets ( e.g. , CIFAR-10/100 , ImageNet-2012 , PASCAL VOC and MS-COCO ) and different convolutional neural network architectures .
Denoising auto - encoders can be stacked to form a deep network by feeding output of the previous layer to the current layer as input . Jain and Seung proposed to use Convolutional Neural Networks ( CNN ) to denoise natural images . Their framework is the same as the recent Fully Convolutional Neural Networks ( FCN ) for semantic image segmentation and other tasks such as super - resolution , although their network is not as deep as today ’s models .
subsection : Token Representation We follow the bi - directional LSTM - CNN architecture , where convolutional neural networks ( CNNs ) encode characters inside a word into character - level representation then concatenated with its word - level into context - independent representation . To further enhance the word representation , we leverage an external representation from pretrained ELMo ( Embeddings from Language Models ) layers according to ELMo ELMo .
A single QRNN layer thus performs an input - dependent pooling , followed by a gated linear combination of convolutional features . As with convolutional neural networks , two or more QRNN layers should be stacked to create a model with the capacity to approximate more complex functions . subsection : Variants
Face recognition performance using deep learning has seen dramatic improvements in recent years . Convolutional networks trained with large datasets of millions of images of thousands of subjects have shown remarkable capability of learning facial representations that are invariant to age , pose , illumination and expression ( A - PIE ) . These representations have shown strong performance for recognition of imagery and video in - the - wild in unconstrained datasets , with recent approaches demonstrating capabilities that exceed human performance on the well known Labeled Faces in the Wild dataset .
A large body of work based on neural networks for text similarity tasks including NLI has been published in recent years . The dominating trend in these models is to build complex , deep text representation models , for example , with convolutional networks or long short - term memory networks with the goal of deeper sentence comprehension . While these approaches have yielded impressive results , they are often computationally very expensive , and result in models having millions of parameters ( excluding embeddings ) .
Specifically , the dual predictions consist of a forward prediction to generate the next word from the current input word , as well as a backward prediction to reconstruct the input word using the predicted word . DPN has two appealing properties : 1 ) By introducing an extra supervision signal on the prediction , DPN can better capture the interplay between the input and the target ; 2 ) Utilizing the reconstructed input , DPN can make another new prediction . During the test phase , we average both predictions to formulate the final target sentence .
These rules are applied in distributed perception networks ( DPN ) , a multi-agent systems approach to distributed Bayesian information fusion . While each DPN agent has limited fusion capabilities , multiple DPN agents can autonomously collaborate to form complex modular fusion systems . Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations .
These rules are applied in distributed perception networks ( DPN ) , a multi agent systems approach to distributed Bayesian information fusion . While each DPN agent has limited fusion capabilities , multiple DPN agents can autonomously collaborate to form complex modular fusion systems . Such self-organizing systems of agents can adapt to the available information sources at runtime and can infer critical hidden events through interpretation of complex patterns consisting of many heterogeneous observations .
Finally we developed a model transformation method that translates a set of synchronous guarded actions – an intermediate format for synchronous systems – to an asynchronous actor description language ( CAL ) . Our theorem ensures that one passed the correctness verification , the generated DPN of asynchronous pro- cesses ( or actors ) preserves the functional behavior of the original synchronous network . Moreover , by the correctness of the synchronous network , our theorem guarantees that the derived DPN is deadlock-free and can be implemented with only finitely bounded buffers .
Finally , we employed a framework for feature learning and classification with the deep polynomial network ( DPN ) on dual-modal features to distinguish between malignant and benign breast tumors . With the leave-one-out cross validation , the DPN method on dual-modal features achieved a sensitivity of 97.8 % , a specificity of 94.1 % , an accuracy of 95.6 % , a Youden 's index of 91.9 % and an area under the receiver operating characteristic curve of 0.961 , which was superior to the classic single-modal methods , and the dual-modal methods using the principal component analysis and multiple kernel learning . These results have demonstrated that the dual-modal AI-based technique with DPN has the potential for breast tumor classification in future clinical practice .
Next , we present an analysis of the requirements for successful introduction of these tools into network operations which is based on exploiting the synergies of multiple technologies , the functional and architectural integra- tion of the tools within the users environments , and the adherence to a software life cycle model which ensures the quality and evolution of the developed tools . Finally , the paper presents a detailed case study which shows the successful application of the above principles to the development of advanced surveillance tools for the DPN packet switching networks .
We have developed a new two-way time transfer modem , which adopts a dual pseudo-random noise ( DPN ) code . The usage of DPN signal dramatically improves time transfer precision without increasing connection fees of commercial communication satellites .
DPN affords concise models that can be conveniently developed and easily transformed into discrete event simulation models . With the help of a simple FMS example , which includes a number of part types , loading rules , dispatching rules , and probabilistic branching ( at an inspection station ) , we illustrate the DPN model development . As an illustration of the ease with which it can be tranformed into a simulation model , we have developed a generalized simulator called ROBSIM and outline here its methodological basis .
In this work , we present a simple , highly efficient and modularized Dual Path Network ( DPN ) for image classification which presents a new topology of connection paths internally . By revealing the equivalence of the state-of-the-art Residual Network ( ResNet ) and Densely Convolutional Network ( DenseNet ) within the HORNN framework , we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations .
The decision plan net ( DPN ) or decision tree ( DT ) is a graphic representation of preferences and the way they are traded off when supply is low . The method allows one to determine compensatory and non-compensatory choice rules that people use .
( d ) with 26 % smaller model size , 25 % less computational cost and 8 % lower memory consumption , and a deeper DPN ( DPN - 131 ) further pushes the state - of - the - art single model performance with about 2 times faster training speed . Experiments on the Places365 large - scale scene dataset , PASCAL VOC detection dataset , and PASCAL VOC segmentation dataset also demonstrate its consistently better performance than DenseNet , ResNet and the latest ResNeXt model over various applications .
Table [ reference ] shows the results for different models . The DPN - 92 costs about fewer parameters than ResNeXt - 101 ( d ) , while the DPN - 98 costs about fewer parameters than ResNeXt - 101 ( d ) .
A Dual-Path Model . In this study , we investigated the effect of personal recognition on the proactive customer service performance .
Methods We propose a novel unified deep learning framework for left atrium segmentation and visualization simultaneously . At first , a novel dual-path module is used to enhance the expressiveness of cardiac image representation . Then a multi-scale context-aware module is designed to effectively handle complex appearance and shape variations of the left atrium and associated pulmonary veins .
To this end , we present Hyper-Pairing Network ( HPN ) , a 3D fully convolution neural network which effectively integrates information from different phases . The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange . Additionally , a pairing loss is added to encourage the commonality between high-level feature representations of different phases .
By revealing the equivalence of the state-of-the-art Residual Network ( ResNet ) and Densely Convolutional Network ( DenseNet ) within the HORNN framework , we find that ResNet enables feature re-usage while DenseNet enables new features exploration which are both important for learning good representations . To enjoy the benefits from both path topologies , our proposed Dual Path Network shares common features while maintaining the flexibility to explore new features through dual path architectures . Extensive experiments on three benchmark datasets , ImagNet-1k , Places365 and PASCAL VOC , clearly demonstrate superior performance of the proposed DPN over state-of-the-arts .
The analysis results along with simulation performed via SMPL show that the proposed scheme is able to enhance the deadline meet ratio of messages by overcoming the network errors . Using the proposed error control scheme , the hard real-time network can be built at cost lower than , but performacne comparable to the dual link network .
We show that a coherent dual-link network can be analyzed as though it is a centralized system . We then discuss practical considerations in implementing a dual-link network , and implications of this work to address problems observed in the IEEE 802.6 metropolitan area network standard .
Based on the dual problem formulation , the recurrent neural network called the dual routing network has much simpler architecture than the one based on the primal problem formulation . The dynamics and architecture of the dual routing network are defined and the operating characteristics are demonstrated via simulation .
The invention discloses a method of releasing a radio bearer in a dual-connection network and a system , which relates to the technical field of radio communication . The problem of releasing a DRB ( Data Radio Bearer ) between a SeNB and UE in the dual-connection network by adopting a 3C frame can be solved . In certain trigger conditions , a MeNB can initiate a DRB releasing process between the SeNB and the UE , the realization method is simple , advantages of the dual-connection technology are used thoroughly , and normal service transmission of the user can be ensured .
The invention relates to a dual-network routing method . The method comprises the following steps : nodes are initialized to establish routing registry connection and generate a routing list ; a first communication link is adopted for transmitting data , and the first communication link is preferentially switched to a second communication link to continue to transmit data in the case of communication link interrupt .
Under certain assumptions about the cost structure and the optimality of the subproblem solutions , the performance guarantees for both the heuristic and linear programming relaxation of the combined overlay optimization model are 33 % . We also develop heuristic and linear programming performance guarantees for specialized models , including a dual path connectivity model with a worst-case performance guarantee of 25 % , and an uncapacitated multicommodity network design model with a worst-case performance guarantee ( approximately ) proportional to the square root of the number of commodities .
They are turned off when not in use to save power . We modify the operation of a pipeline stage to accommodate the dual-path scheme by using time-interleaving capacitor sets . Operating at 200 MS/s sampling rate , this ADC consumes 5.37 mW from a 1 V supply .
The frequency-separated property is analyzed , and it shows the capability to design the filtering function separately in each path . Moreover , the dual-path topology is compatible with both symmetric and asymmetric dual-band characteristic . Two microstrip implementations are provided to demonstrate the reliability of dual-path dual-band topology .
document : Dual Path Networks In this work , we present a simple , highly efficient and modularized Dual Path Network ( DPN ) for image classification which presents a new topology of connection paths internally .
Although the width of the densely connected path increases linearly as it goes deeper , causing the number of parameters to grow quadratically , DenseNet provides higher parameter efficiency compared with the ResNet . In this work , we aim to study the advantages and limitations of both topologies and further enrich the path design by proposing a dual path architecture . In particular , we first provide a new understanding of the densely connected networks from the lens of a higher order recurrent neural network ( HORNN ) , and explore the relations between densely connected networks and residual networks .
In this work , we aim to provide a deeper understanding of the densely connected network , from the lens of Higher Order RNN , and explain how the residual networks are in indeed a special case of densely connected network . Based on these analysis , we then propose a novel Dual Path Network architecture that not only achieves higher accuracy , but also enjoys high parameter and computational efficiency . section : Revisiting ResNet , DenseNet and Higher Order RNN
However , different may extract the same type of features multiple times , leading to high redundancy . In the following section , we present the dual path networks which can overcome both inherent limitations of these two state - of - the - art network architectures . Their relations with HORNN also imply that our proposed architecture can be used for improving HORNN , which we leave for future works .
In the following , we first introduce and formulate the dual path architecture , and then present the network structure in details with complexity analysis . subsection : Dual Path Architecture Sec .
Recently , the equivalence to RNNs has been further extended to DenseNet . Based on this finding , Dual Path Networks were proposed and showed superior performance to DenseNet and ResNet in a varity of applications . section : Single - State Recurrent Networks
Computational optimization of convolutional neural networks using separated filters architecture E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet Deep Regionlets for Object Detection Hybrid-loss supervision for deep neural network Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Dependency or Span, End-to-End Uniform Semantic Role Labeling Quasi-Recurrent Neural Networks Template adaptation for face verification and identification A Decomposable Attention Model for Natural Language Inference A Dual Prediction Network for Image Captioning A multi-agent systems approach to distributed bayesian information fusion A Distributed Approach to Information Fusion Systems Based on Causal Probabilistic Models Model-based Design of Embedded Systems by Desynchronization Dual-mode artificially-intelligent diagnosis of breast tumours in shear-wave elastography and B-mode ultrasound using deep polynomial networks. Intelugent tools for network management Development of a GPU based two-way time transfer modem Modeling FMS with decision Petri nets Dual Path Networks The Decision Plan Nets Method Dual Path Networks Personal Recognition and Proactive Customer Service Performance: A Dual-Path Model Segmentation and visualization of left atrium through a unified deep learning framework Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation Dual Path Networks A Best-Effort Control Scheme on FDDI-Based Real-Time Data Collection Networks Analysis of Dual-Link Networks for Real-Time Applications A dual neural network for shortest-path routing Method of releasing radio bearer in dual-connection network and system Power line and wireless dual-network routing method Heuristics, LPs, and Generalizations of Trees on Trees A CMOS 5.37-mW 10-Bit 200-MS/s Dual-Path Pipelined ADC Dual-band filters with frequency-separated dual-path coupling scheme Dual Path Networks Image Super-Resolution via Dual-State Recurrent Networks
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ArXiv AAAI ICLR Image Vis. Comput. EMNLP ICME NIPS ICNN NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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