However , by nature , workers need to frequently communicate gradients , causing severe bottlenecks , especially on lower bandwidth connections . A few methods have been proposed to compress gradient for efficient communication , but they either suffer a low compression ratio or significantly harm the resulting model accuracy , particularly when applied to convolutional neural networks . To address these issues , we propose a method to reduce the communication overhead of distributed deep learning . For sensor networks we focus on the pros and cons of mobile versus static sensing strategies , and the description of a low-cost solution to acoustic sensing that supports distributed machine listening . For sound event detection and classification we focus on the challenges presented by this task , solutions including feature design and learning strategies , and how a combination of convolutional networks and data augmentation result in the current state of the art . We close with a discussion about the potential and challenges of mobile sensing , the limitations imposed by the data currently available for research , and a few areas for future exploration . Image decomposition techniques such as Principal Component Analysis have been used for a long time , evolving towards complex non-linear decomposition algorithms . Convolutional networks , and particularly convolutional autoen-coders are known for providing high-level abstract features that describe the internal distribution of data in high-dimensional manifolds . In this work , we aim at gaining a deeper understanding the progression of the disease by predicting neuropsycho-logical test outcomes based solely on MRI data . Autoimmune diseases are the third highest cause of mortality in the world , and the identification of an anti-nuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis . In this work , we assess the performance of six preprocessing strategies and five state-of-the-art convolutional neural network architectures for the classification of HEp-2 cells . We also evaluate enhancement methods such as hyperparameter optimization , data augmentation , and fine-tuning training strategies . Various techniques are available for solving the non-trivial problem of edge detection . In this paper , the various convolutional neural networks based techniques for edge detection are explored . Furthermore , visual and motion enhancement methods are applied on color images to enhance their local patterns . Third , a convolutional neural networks-based model is adopted to extract robust and discriminative features from color images . The final action class scores are generated by decision level fusion of deep features . We use pre - trained word vectors GloVe to obtain the fixed word embedding of each word . The character - level embeddings are generated by using Convolutional Neural Networks ( CNN ) which is applied to the characters of each word ( Kim , et al . , 2014 ) . This layer maps each token to a high dimensional vector space and is proved to be helpful in handling out - of - vocab ( OOV ) words . None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks . [ reference ] and [ reference ] propose heuristics based on uncertainty and gradient magnitudes , respectively , and apply their methods to convolutional neural networks . Another recent work uses multi - agent reinforcement learning [ reference ] . For this reason , this model has more abilities of catching intrinsic data patterns and leads to better performance . The idea using FM in the bottom layer is ignited by Convolutional Neural Networks ( CNNs ) , which exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers . Similarly , the inputs of hidden layer 1 are connected to the input units of a specific field . section : Background We provide a brief introduction to the required background in convolutional networks and graph theory . subsection : Convolutional Neural Networks In this paper , we introduce a novel methodology for characterizing the performance of deep learning networks ( ResNets and DenseNet ) with respect to training convergence and generalization as a function of mini-batch size and learning rate for image classification . This methodology is based on novel measurements derived from the eigenvalues of the approximate Fisher information matrix , which can be efficiently computed even for high capacity deep models . In clinical diagnosis , automatic fracture detection can reduce misdiagnosis which caused by fatigue and inexperience of radiologists , and provide support for reducing patient suffering and preventing disease progression . This paper proposes a SK-DenseNet model to detect femur fracture based on DenseNet network . It uses SK module to adjust the size of receptive fields adaptively , adopts Focal loss function to update parameter , and uses Grad-CAM method to visualize the femoral detection results . For achieving the goal , we used Densely Connected Convolution Neural Network ( DenseNet ) . Various experiments were conducted to determine a justifiable DenseNet network with optimal parameters . We achieved an accuracy of 98.00 % on the training set , 97.20 % on the validation set and 97.65 % on the test set , with a precision of 0.9640 , recall/sensitivity of 0.9557 and specificity of 0.9915 . The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma , Lobular carcinoma , etc . Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model achieved high processing performances with 95.4 % of accuracy in the multi-class breast cancer classification task when compared with state-of-the-art models . Firstly , a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification . Then , we construct a 3D Densely Connected Convolutional Networks ( 3D DenseNet ) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task . Finally , the learned features from the multi-task CNN and DenseNet models are combined to classify disease status . However , such architectures usually have millions of parameters and inadequate number of training samples leading to over‐fitting and poor generalization . In this paper , we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient . We propose a novel up‐sampling path which incorporates long skip and short‐cut connections to overcome the feature map explosion in conventional FCN based architectures . Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that captures spatial correlations . In this paper , we propose a novel multiple feature reweight DenseNet ( MFR-DenseNet ) architecture . The MFR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between the features of different convolutional layers . In this paper , we propose a fully convolutional DenseNet model for saliency-map prediction ( DenseSal ) . While the most state-of-the-art models for predicting saliency maps use shallow networks such as VGG-16 , our model uses densely connected convolutional networks ( DenseNet ) with over 150 layers . The accuracy of the modified CNNBCN model for brain tumor classification reaches 95.49 % , which is higher than several models presented by other works . In addition , the test loss of brain tumor classification of the modified CNNBCN model is lower than those of the ResNet , DenseNet and MobileNet models in the experiments . The modified CNNBCN model not only achieves satisfactory results in brain tumor image classification , but also enriches the methodology of neural network design . To validate the effectiveness of VoVNet as a backbone network , we design both lightweight and large-scale VoVNet and apply them to one-stage and two-stage object detectors . Our VoVNet based detectors outperform DenseNet based ones with 2 × faster speed and the energy consumptions are reduced by 1.6 × - 4.1 × . In addition to DenseNet , VoVNet also outperforms widely used ResNet backbone with faster speed and better energy efficiency . Secondly , the DenseNet neural network model is improved , and a new DenseNet-II neural network model is invented , which is to replace the first convolutional layer of the DenseNet neural network model with the Inception structure . Finally , the pre-processed mammogram datasets are input into AlexNet , VGGNet , GoogLeNet , DenseNet network model and DenseNet-II neural network model , and the experimental results are analyzed and compared . According to the 10-fold cross validation method , the results show that the DenseNet-II neural network model has better classification performance than other network models . Feature Extraction Layer adopts feature extractor to extract the semantic feature from interaction tensor . The convolutional feature extractors , such as AlexNet , VGG , Inception , ResNet and DenseNet , proven work well on image recognition are completely compatible under such architecture . Unlike the work who employs 1 - D sliding window , our CNN architecture allows 2 - D kernel to extract semantic interaction feature from the word - by - word interaction between n - gram pair . Instead , they proposed the DRRN that applies both global and local learning while remaining parameter efficient via recursive learning . More recently , Tong et al . proposed making use of Densely Connected Networks ( DenseNet ) instead of ResNet as the building block for image SR . They demonstrated that the DenseNet structure is better at combining features at different levels , which boosts SR performance . We also compare our method with the DenseNet . Compared with the DenseNet - 161 , the best performing model of the officially provided DenseNet family , the Res2Net - 101 has an improvement of 1.54 in terms of top - 1 error , even though the DenseNet - 161 requires nearly 100 M more memory than the Res2Net - 101 does for an image of pixels . subsubsection : Effectiveness of scale dimension . Res2NeXt - 29 , 6c×24w×4scale even outperforms the ResNeXt - 29 , 16c×64w with only 35 parameters . We also achieve better performance with fewer parameters , compared with DenseNet - BC ( k = 40 ) . Note that DenseNet is memory - consuming compared to our method . We fine - tune from three baseline models using the PC optimization procedure : ResNet - 50 [ reference ] , Bilinear CNN [ reference ] , and DenseNet - 161 [ reference ] . As Tables 2 -( A - F ) show , PC obtains substantial improvement across all datasets and models . subsection : Submanifold Convolutional Networks We use a combination of VSC convolutions , strided SC convolutions , and sparse pooling operations to build sparse versions of the popular VGG , ResNet , and DenseNet convolutional networks . The blocks we use in our networks are presented in Figure [ reference ] . Herein , the word dense does not refer to a lack of spatial sparsity but rather to the pattern of connections between convolution operations . A simple DenseNet module is a sequence of convolutions in which each convolution takes as input the concatenated output of all the previous convolution operations . The bottleneck layers in our submanifold DenseNets are implemented in the same way as for ResNets . To implement a VSC convolution , we re - use the input hash table for the output , and construct an appropriate rule book . Note that because the sparsity pattern does not change , the same rule book can be re - used in VGG / ResNet / DenseNet networks until a pooling or subsampling layer is encountered . If there are active points in the input layer , the cost of building the input hash - table is . For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of . This can improve gradient flow and convergence properties , especially in deeper networks , although it requires a parameter count that is quadratic in the number of layers . We mainly focus on the first step for improving heatmap prediction accuracy . We propose Higher-Resolution Network ( HigherHRNet ) , which is a simple extension of the High-Resolution Network ( HRNet ) . HigherHRNet generates higher-resolution feature maps by deconvolving the high-resolution feature maps outputted by HRNet , which are spatially more accurate for small and medium persons . In this paper , we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network ( CNN ) model . We propose CascadeTabNet : a Cascade mask Region-based CNN High-Resolution Network ( Cascade mask R-CNN HRNet ) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time . We evaluate our results on ICDAR 2013 , ICDAR 2019 and Table-Bank public datasets . Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series ( e.g. , ResNet , VGGNet ) , and then recover the high-resolution representation from the encoded low-resolution representation . Instead , our proposed network , named as High-Resolution Network ( HRNet ) , maintains high-resolution representations through the whole process . There are two key characteristics : ( i ) Connect the high-to-low resolution convolution streams in parallel ; ( ii ) Repeatedly exchange the information across resolutions . In this paper , we proposed a novel solution combining the convolutional neural network ( CNN ) and heatmap regression to detect unstructured road VP . The proposed algorithm firstly adopts a lightweight backbone , i.e. , depthwise convolution modified HRNet , to extract hierarchical features of the unstructured road image . Then , three advanced strategies , i.e. , multi-scale supervised learning , heatmap super-resolution , and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection . HRNet can preserve the information of small target nodules in the feature map with high resolution and obtain a finegrained feature map for the detection task . This paper also combines dilated convolution with HRNet and proposes an improved HRNet named dilated HRNet . Experiments on the LIDC-IDRI dataset show that the improved Cascade R-CNN increases the detection accuracy of pulmonary nodules , especially on small nodules . Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction . In this work , we present a Multiple Stage High-Resolution Network ( Multi-Stage HRNet ) to tackling the problem of multi-person pose estimation in images . Specifically , we follow the top-down pipelines and high-resolution representations are maintained during single-person pose estimation . The proposed algorithm combines the real-time instance semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet . Firstly , feature maps are extracted by HRNet 's backbone network . Secondly , the feature pyramid network is used to further extract image features , so that the network has better classification ability . We introduce a light-weight , power efficient , and general purpose convolutional neural network , ESPNetv2 , for modeling visual and sequential data . Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters .
However , by nature , workers need to frequently communicate gradients , causing severe bottlenecks , especially on lower bandwidth connections . A few methods have been proposed to compress gradient for efficient communication , but they either suffer a low compression ratio or significantly harm the resulting model accuracy , particularly when applied to convolutional neural networks . To address these issues , we propose a method to reduce the communication overhead of distributed deep learning .
For sensor networks we focus on the pros and cons of mobile versus static sensing strategies , and the description of a low-cost solution to acoustic sensing that supports distributed machine listening . For sound event detection and classification we focus on the challenges presented by this task , solutions including feature design and learning strategies , and how a combination of convolutional networks and data augmentation result in the current state of the art . We close with a discussion about the potential and challenges of mobile sensing , the limitations imposed by the data currently available for research , and a few areas for future exploration .
Image decomposition techniques such as Principal Component Analysis have been used for a long time , evolving towards complex non-linear decomposition algorithms . Convolutional networks , and particularly convolutional autoen-coders are known for providing high-level abstract features that describe the internal distribution of data in high-dimensional manifolds . In this work , we aim at gaining a deeper understanding the progression of the disease by predicting neuropsycho-logical test outcomes based solely on MRI data .
Autoimmune diseases are the third highest cause of mortality in the world , and the identification of an anti-nuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis . In this work , we assess the performance of six preprocessing strategies and five state-of-the-art convolutional neural network architectures for the classification of HEp-2 cells . We also evaluate enhancement methods such as hyperparameter optimization , data augmentation , and fine-tuning training strategies .
Various techniques are available for solving the non-trivial problem of edge detection . In this paper , the various convolutional neural networks based techniques for edge detection are explored .
Furthermore , visual and motion enhancement methods are applied on color images to enhance their local patterns . Third , a convolutional neural networks-based model is adopted to extract robust and discriminative features from color images . The final action class scores are generated by decision level fusion of deep features .
We use pre - trained word vectors GloVe to obtain the fixed word embedding of each word . The character - level embeddings are generated by using Convolutional Neural Networks ( CNN ) which is applied to the characters of each word ( Kim , et al . , 2014 ) . This layer maps each token to a high dimensional vector space and is proved to be helpful in handling out - of - vocab ( OOV ) words .
None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks . [ reference ] and [ reference ] propose heuristics based on uncertainty and gradient magnitudes , respectively , and apply their methods to convolutional neural networks . Another recent work uses multi - agent reinforcement learning [ reference ] .
For this reason , this model has more abilities of catching intrinsic data patterns and leads to better performance . The idea using FM in the bottom layer is ignited by Convolutional Neural Networks ( CNNs ) , which exploit spatially local correlation by enforcing a local connectivity pattern between neurons of adjacent layers . Similarly , the inputs of hidden layer 1 are connected to the input units of a specific field .
section : Background We provide a brief introduction to the required background in convolutional networks and graph theory . subsection : Convolutional Neural Networks
In this paper , we introduce a novel methodology for characterizing the performance of deep learning networks ( ResNets and DenseNet ) with respect to training convergence and generalization as a function of mini-batch size and learning rate for image classification . This methodology is based on novel measurements derived from the eigenvalues of the approximate Fisher information matrix , which can be efficiently computed even for high capacity deep models .
In clinical diagnosis , automatic fracture detection can reduce misdiagnosis which caused by fatigue and inexperience of radiologists , and provide support for reducing patient suffering and preventing disease progression . This paper proposes a SK-DenseNet model to detect femur fracture based on DenseNet network . It uses SK module to adjust the size of receptive fields adaptively , adopts Focal loss function to update parameter , and uses Grad-CAM method to visualize the femoral detection results .
For achieving the goal , we used Densely Connected Convolution Neural Network ( DenseNet ) . Various experiments were conducted to determine a justifiable DenseNet network with optimal parameters . We achieved an accuracy of 98.00 % on the training set , 97.20 % on the validation set and 97.65 % on the test set , with a precision of 0.9640 , recall/sensitivity of 0.9557 and specificity of 0.9915 .
The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma , Lobular carcinoma , etc . Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model achieved high processing performances with 95.4 % of accuracy in the multi-class breast cancer classification task when compared with state-of-the-art models .
Firstly , a multi-task deep CNN model is constructed for jointly learning hippocampal segmentation and disease classification . Then , we construct a 3D Densely Connected Convolutional Networks ( 3D DenseNet ) to learn features of the 3D patches extracted based on the hippocampal segmentation results for the classification task . Finally , the learned features from the multi-task CNN and DenseNet models are combined to classify disease status .
However , such architectures usually have millions of parameters and inadequate number of training samples leading to over‐fitting and poor generalization . In this paper , we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient . We propose a novel up‐sampling path which incorporates long skip and short‐cut connections to overcome the feature map explosion in conventional FCN based architectures .
Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that captures spatial correlations . In this paper , we propose a novel multiple feature reweight DenseNet ( MFR-DenseNet ) architecture . The MFR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between the features of different convolutional layers .
In this paper , we propose a fully convolutional DenseNet model for saliency-map prediction ( DenseSal ) . While the most state-of-the-art models for predicting saliency maps use shallow networks such as VGG-16 , our model uses densely connected convolutional networks ( DenseNet ) with over 150 layers .
The accuracy of the modified CNNBCN model for brain tumor classification reaches 95.49 % , which is higher than several models presented by other works . In addition , the test loss of brain tumor classification of the modified CNNBCN model is lower than those of the ResNet , DenseNet and MobileNet models in the experiments . The modified CNNBCN model not only achieves satisfactory results in brain tumor image classification , but also enriches the methodology of neural network design .
To validate the effectiveness of VoVNet as a backbone network , we design both lightweight and large-scale VoVNet and apply them to one-stage and two-stage object detectors . Our VoVNet based detectors outperform DenseNet based ones with 2 × faster speed and the energy consumptions are reduced by 1.6 × - 4.1 × . In addition to DenseNet , VoVNet also outperforms widely used ResNet backbone with faster speed and better energy efficiency .
Secondly , the DenseNet neural network model is improved , and a new DenseNet-II neural network model is invented , which is to replace the first convolutional layer of the DenseNet neural network model with the Inception structure . Finally , the pre-processed mammogram datasets are input into AlexNet , VGGNet , GoogLeNet , DenseNet network model and DenseNet-II neural network model , and the experimental results are analyzed and compared . According to the 10-fold cross validation method , the results show that the DenseNet-II neural network model has better classification performance than other network models .
Feature Extraction Layer adopts feature extractor to extract the semantic feature from interaction tensor . The convolutional feature extractors , such as AlexNet , VGG , Inception , ResNet and DenseNet , proven work well on image recognition are completely compatible under such architecture . Unlike the work who employs 1 - D sliding window , our CNN architecture allows 2 - D kernel to extract semantic interaction feature from the word - by - word interaction between n - gram pair .
Instead , they proposed the DRRN that applies both global and local learning while remaining parameter efficient via recursive learning . More recently , Tong et al . proposed making use of Densely Connected Networks ( DenseNet ) instead of ResNet as the building block for image SR . They demonstrated that the DenseNet structure is better at combining features at different levels , which boosts SR performance .
We also compare our method with the DenseNet . Compared with the DenseNet - 161 , the best performing model of the officially provided DenseNet family , the Res2Net - 101 has an improvement of 1.54 in terms of top - 1 error , even though the DenseNet - 161 requires nearly 100 M more memory than the Res2Net - 101 does for an image of pixels . subsubsection : Effectiveness of scale dimension .
Res2NeXt - 29 , 6c×24w×4scale even outperforms the ResNeXt - 29 , 16c×64w with only 35 parameters . We also achieve better performance with fewer parameters , compared with DenseNet - BC ( k = 40 ) . Note that DenseNet is memory - consuming compared to our method .
We fine - tune from three baseline models using the PC optimization procedure : ResNet - 50 [ reference ] , Bilinear CNN [ reference ] , and DenseNet - 161 [ reference ] . As Tables 2 -( A - F ) show , PC obtains substantial improvement across all datasets and models .
subsection : Submanifold Convolutional Networks We use a combination of VSC convolutions , strided SC convolutions , and sparse pooling operations to build sparse versions of the popular VGG , ResNet , and DenseNet convolutional networks . The blocks we use in our networks are presented in Figure [ reference ] .
Herein , the word dense does not refer to a lack of spatial sparsity but rather to the pattern of connections between convolution operations . A simple DenseNet module is a sequence of convolutions in which each convolution takes as input the concatenated output of all the previous convolution operations . The bottleneck layers in our submanifold DenseNets are implemented in the same way as for ResNets .
To implement a VSC convolution , we re - use the input hash table for the output , and construct an appropriate rule book . Note that because the sparsity pattern does not change , the same rule book can be re - used in VGG / ResNet / DenseNet networks until a pooling or subsampling layer is encountered . If there are active points in the input layer , the cost of building the input hash - table is .
For sequence classification tasks , we found it helpful to use skip - connections between every QRNN layer , a technique termed “ dense convolution ” by . Where traditional feed - forward or convolutional networks have connections only between subsequent layers , a “ DenseNet ” with layers has feed - forward or convolutional connections between every pair of layers , for a total of . This can improve gradient flow and convergence properties , especially in deeper networks , although it requires a parameter count that is quadratic in the number of layers .
We mainly focus on the first step for improving heatmap prediction accuracy . We propose Higher-Resolution Network ( HigherHRNet ) , which is a simple extension of the High-Resolution Network ( HRNet ) . HigherHRNet generates higher-resolution feature maps by deconvolving the high-resolution feature maps outputted by HRNet , which are spatially more accurate for small and medium persons .
In this paper , we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network ( CNN ) model . We propose CascadeTabNet : a Cascade mask Region-based CNN High-Resolution Network ( Cascade mask R-CNN HRNet ) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time . We evaluate our results on ICDAR 2013 , ICDAR 2019 and Table-Bank public datasets .
Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series ( e.g. , ResNet , VGGNet ) , and then recover the high-resolution representation from the encoded low-resolution representation . Instead , our proposed network , named as High-Resolution Network ( HRNet ) , maintains high-resolution representations through the whole process . There are two key characteristics : ( i ) Connect the high-to-low resolution convolution streams in parallel ; ( ii ) Repeatedly exchange the information across resolutions .
In this paper , we proposed a novel solution combining the convolutional neural network ( CNN ) and heatmap regression to detect unstructured road VP . The proposed algorithm firstly adopts a lightweight backbone , i.e. , depthwise convolution modified HRNet , to extract hierarchical features of the unstructured road image . Then , three advanced strategies , i.e. , multi-scale supervised learning , heatmap super-resolution , and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection .
HRNet can preserve the information of small target nodules in the feature map with high resolution and obtain a finegrained feature map for the detection task . This paper also combines dilated convolution with HRNet and proposes an improved HRNet named dilated HRNet . Experiments on the LIDC-IDRI dataset show that the improved Cascade R-CNN increases the detection accuracy of pulmonary nodules , especially on small nodules .
Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction . In this work , we present a Multiple Stage High-Resolution Network ( Multi-Stage HRNet ) to tackling the problem of multi-person pose estimation in images . Specifically , we follow the top-down pipelines and high-resolution representations are maintained during single-person pose estimation .
The proposed algorithm combines the real-time instance semantic segmentation algorithm YOLACT with the deep high-resolution representation classification network HRNet . Firstly , feature maps are extracted by HRNet 's backbone network . Secondly , the feature pyramid network is used to further extract image features , so that the network has better classification ability .
We introduce a light-weight , power efficient , and general purpose convolutional neural network , ESPNetv2 , for modeling visual and sequential data . Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters .
Variance-based Gradient Compression for Efficient Distributed Deep Learning Sound analysis in smart cities A Deep Decomposition of MRI to Explore Neurodegeneration in Alzheimer’s Disease Comparing convolutional neural networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images A Survey on Convolutional Neural Network based Edge Detection Techniques Enhanced skeleton visualization for view invariant human action recognition MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Multi-Task Learning as Multi-Objective Optimization Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Learning Convolutional Neural Networks for Graphs Approximate Fisher Information Matrix to Characterize the Training of Deep Neural Networks A Method for Detecting Femur Fracture Based on SK-DenseNet Detection of Retinal Disorders in Optical Coherence Tomography using Deep Learning Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease Fully convolutional multi‐scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers Multiple Feature Reweight DenseNet for Image Classification Fully Convolutional DenseNet for Saliency-Map Prediction Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection Benign and malignant classification of mammogram images based on deep learning Natural Language Inference over Interaction Space Image Super-Resolution via Dual-State Recurrent Networks Res2Net: A New Multi-scale Backbone Architecture Pairwise Confusion for Fine-Grained Visual Classification Submanifold Sparse Convolutional Networks Quasi-Recurrent Neural Networks Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents Deep High-Resolution Representation Learning for Visual Recognition Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation A Real-Time Detection Drone Algorithm Based on Instance Semantic Segmentation ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network
707f536610cc2ee6bb27471355e0e16245ad5d90 7fdf187adb17560855b065ebe1ddbe539d8a8207 00cdb753bbbf3ceba4bf498449bf456f58cfd817 08abba260dab33dca6e24d93808086c3cc65adfd aec503828a0501409a164d40cf77bf71e59d1578 928742fae8f5f6e3142cdab1976e9198e21092c6 7fb3bd7b9d0cf68dee44254a28bd0af528cc6221 b962eb1a6295f3cd5bed2d9ce67674cdad8edd9e e96db6a14b73f33d5af27a7cfe090c52dfa7ec6a 1a19f55bc7f5d3d90bd5cb239c0202b3428b6aa2 139ee3fbb8a30c642a789786faaf9e11b76001de 1acdfdf6e24dc6c89f61aa7600648b38870bbc9b 6d4e70673b180f874038265da8ada29872daa648 d702248cf46124600c680608a510e03a30b4d63b ac5a9771dcb832c5574cad279865d6094482e808 b517f179ffd609247f0665e7247fbebb7a4e8ea3 68e98184ac29cb50df57b4cce8462debea8722ad a5aeebd6182253da7469cf5e112590af821942dd d0b001b95c1e2074777553bb9aee5e8edb07fa43 441555b5cd09703e55c03e70bd2c9f82c0ffcf9b 53a06bdf9c0f110beeb9feba941640607d20fb56 19eeef58f26843fb1c7555a3361fe0edb0d9c740 ef2cbd168937e84d32187409016e3df7cdcafc06 ee0c6dfc919486b4c8fd1c9cc819974905a93ffb 0a0262328b74b6f59d3d85c0ad2606977af1a22a
["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Mathematics","Computer Science"] ["Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Psychology","Medicine","Computer Science"] ["Computer Science","Mathematics","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2018 2018 2018 2020 2017 2017 2017 2018 2016 2016 2020 2019 2019 2018 2020 2018 2019 2017 2020 2019 2019 2018 2018 2019 2018 2017 2017 2019 2020 2019 2020 2020 2019 2019 2018
ArXiv NeurIPS ECIR ICML AIAM ICCCNT ACPR ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE transactions on pattern analysis and machine intelligence ECCV ArXiv ICLR ICMLC ICVIP
https://api.semanticscholar.org/707f536610cc2ee6bb27471355e0e16245ad5d90 https://api.semanticscholar.org/7fdf187adb17560855b065ebe1ddbe539d8a8207 https://api.semanticscholar.org/00cdb753bbbf3ceba4bf498449bf456f58cfd817 https://api.semanticscholar.org/08abba260dab33dca6e24d93808086c3cc65adfd https://api.semanticscholar.org/aec503828a0501409a164d40cf77bf71e59d1578 https://api.semanticscholar.org/928742fae8f5f6e3142cdab1976e9198e21092c6 https://www.semanticscholar.org/paper/12e20e4ea572dbe476fd894c5c9a9930cf250dd2 https://www.semanticscholar.org/paper/2b0d7e51efd004fe3847f54863540c79312f3546 https://www.semanticscholar.org/paper/1bc072002d97808340b312b69427baf2dc9fcb8e https://www.semanticscholar.org/paper/7c6de5a9e02a779e24504619050c6118f4eac181 https://api.semanticscholar.org/7fb3bd7b9d0cf68dee44254a28bd0af528cc6221 https://api.semanticscholar.org/b962eb1a6295f3cd5bed2d9ce67674cdad8edd9e https://api.semanticscholar.org/e96db6a14b73f33d5af27a7cfe090c52dfa7ec6a https://api.semanticscholar.org/1a19f55bc7f5d3d90bd5cb239c0202b3428b6aa2 https://api.semanticscholar.org/139ee3fbb8a30c642a789786faaf9e11b76001de https://api.semanticscholar.org/1acdfdf6e24dc6c89f61aa7600648b38870bbc9b https://api.semanticscholar.org/6d4e70673b180f874038265da8ada29872daa648 https://api.semanticscholar.org/d702248cf46124600c680608a510e03a30b4d63b https://api.semanticscholar.org/ac5a9771dcb832c5574cad279865d6094482e808 https://api.semanticscholar.org/b517f179ffd609247f0665e7247fbebb7a4e8ea3 https://api.semanticscholar.org/68e98184ac29cb50df57b4cce8462debea8722ad https://www.semanticscholar.org/paper/1778e32c18bd611169e64c1805a51abff341ca53 https://www.semanticscholar.org/paper/175f74a09241b6cb5101a2a09978095720db7d5f https://www.semanticscholar.org/paper/2a69ddbafb23c63e5e22401664bea229daaeb7d6 https://www.semanticscholar.org/paper/20cc4bfdb648fd7947c71252589fc867d4d16933 https://www.semanticscholar.org/paper/11356cd6bb0f2776a88cd584ff108470414c6594 https://www.semanticscholar.org/paper/2d876ed1dd2c58058d7197b734a8e4d349b8f231 https://api.semanticscholar.org/a5aeebd6182253da7469cf5e112590af821942dd https://api.semanticscholar.org/d0b001b95c1e2074777553bb9aee5e8edb07fa43 https://api.semanticscholar.org/441555b5cd09703e55c03e70bd2c9f82c0ffcf9b https://api.semanticscholar.org/53a06bdf9c0f110beeb9feba941640607d20fb56 https://api.semanticscholar.org/19eeef58f26843fb1c7555a3361fe0edb0d9c740 https://api.semanticscholar.org/ef2cbd168937e84d32187409016e3df7cdcafc06 https://api.semanticscholar.org/ee0c6dfc919486b4c8fd1c9cc819974905a93ffb https://api.semanticscholar.org/0a0262328b74b6f59d3d85c0ad2606977af1a22a