We evaluated micro and macro-F1 scores of four information extraction tasks , including subsite , laterality , behavior , and histological grades from cancer pathology reports . The scores for the Text GCN significantly outperformed our previous studies with convolutional neural networks , suggesting that the Text GCN model is superior to traditional models in task performance . Two-stream architecture have shown strong performance in video classification task . The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally . However , there are some problems within such architecture . Learning non-parametric invariances directly from data remains an important open problem . In this paper , we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself . This layer can learn invariance to non-parametric transformations and interestingly , motivates and incorporates permanent random connectomes , thereby being called Permanent Random Connectome Non-Parametric Transformation Networks ( PRC-NPTN ) . The dataset contains RGB images from young carrot seedlings taken during the period of February in the area around Negotino , Republic of Macedonia . We performed initial analysis of the dataset and report the initial results , obtained using convolutional neural network architectures . It was shown that the custom image processing pipeline , which achieved a worm segmentation accuracy of 94 % , was outperformed by all of the architectures considered , with the best being 97.3 % achieved by a FCN with a single downsampling layer . These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans . These scenes have not been well-handled in computer vision tasks . Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers ; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches . In this paper , we proposed a new CNN learning approach to categorize skyline scenes . Thus , we should expect early sensory processing to supply classification information fast and accurately , even before relying the information to higher brain areas or more complex system components downstream . Today , advanced convolutional artificial neural networks can successfully solve visual detection and classification tasks and are commonly used to build complex decision making systems . However , in order to perform well on these tasks they require increasingly complex , " very deep " model structure , which is costly in inference run-time , energy consumption and number of training samples , only trainable on cloud-computing clusters . Luckily , current GPUs , paired with a highly - optimized implementation of 2D convolution , are powerful enough to facilitate the training of interestingly - large CNNs , and recent datasets such as ImageNet contain enough labeled examples to train such models without severe overfitting . The specific contributions of this paper are as follows : we trained one of the largest convolutional neural networks to date on the subsets of ImageNet used in the ILSVRC - 2010 and ILSVRC - 2012 competitions [ 2 ] and achieved by far the best results ever reported on these datasets . We wrote a highly - optimized GPU implementation of 2D convolution and all the other operations inherent in training convolutional neural networks , which we make available publicly1 . document : Quaternion Convolutional Neural Networks for End - to - End Automatic Speech Recognition Recently , the connectionist temporal classification ( CTC ) model coupled with recurrent ( RNN ) or convolutional neural networks ( CNN ) , made it easier to train speech recognition systems in an end - to - end fashion . However in real - valued models , time frame components such as mel - filter - bank energies and the cepstral coefficients obtained from them , together with their first and second order derivatives , are processed as individual elements , while a natural alternative is to process such components as composed entities . document : Deep Label Distribution Learning With Label Ambiguity Convolutional Neural Networks ( ConvNets ) have achieved excellent recognition performance in various visual recognition tasks . A large labeled training set is one of the most important factors for its success . This section considers the final , global spatial pooling layer . Local pooling operators , usually max pooling , are found throughout the layers of most convolutional networks to achieve local invariance to small translations . By contrast , global spatial pooling converts a 3D tensor of activations produced by a convolutional trunk to a vector . Pose Machines provide a sequential prediction framework for learning rich implicit spatial models . In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image - dependent spatial models for the task of pose estimation . The contribution of this paper is to implicitly model long - range dependencies between variables in structured prediction tasks such as articulated pose estimation . There have been several attempts to use convolutional encoder models for neural machine trans - lation in the past but they were either only applied to rescoring n - best lists of classical systems [ reference ] or were not competitive to recurrent alternatives [ reference ] . This is despite several attractive properties of convolutional networks . For example , convolutional networks operate over a fixed - size window of the input sequence which enables the simultaneous computation of all features for a source sentence . At the first level , each cluster of users can be connected either via a ShuffleNet ( SH ) or a de Bruijn network ( dB ) . At the second level , all the clusters in the system can be connected by two rings in opposite directions ( SH/Ring and dB/Ring ) , or a de Bruijn network ( dB/dB ) , or a ShuffleNet ( SH/SH ) . The performances of the ShuffleNet , the de Bruijn networks , and the hierarchical networks : SH/Ring , dB/Ring , dB/dB and SH/SH , are compared . The loop-augmented ShuffleNet is constructed with slight change in the ShuffleNet connectivity while retaining the simple addressing and self-routing properties . Compared with ShuffleNet , the network performance is greatly improved by reducing the expected number of hops using this new network design . In a multi-star network , however , one does not have the same degree of freedom for reconfiguration because one is not free to exchange the logical identities of an arbitrary pair of nodes . In the paper , the authors investigate the reconfigurability issue in the multi-star implementation of ShuffleNet . They show how one can attain maximum reconfigurability by properly choosing the system parameters . Modular growth of ShuffleNets , however , is generally difficult and requires many hardware and software reconfigurations . The authors consider a multi-star implementation of ShuffleNet and discuss how a ( p , k ) ShuffleNet can be expanded to a ( p , k+1 ) ShuffleNet in modular phases , where each phase increases the number of nodes by only a small fraction and requires only minor hardware and software reconfigurations.<<ETX > > Moreover , YOLOv3 was improved to obtain more effective detection results . First , we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2 . Second , the prediction box center point , XY loss , and prediction box width and height , WH loss , in the loss function was replaced with the generalized intersection over union ( GIoU ) loss . Logical topologies proposed in the literature include the ShuffleNet , De Bruijn graph , Manhattan Street Network , and Hypercube . We show that for a given nodal in-degree and out-degree and average number of hops between stations , a network based on Kautz graphs can support a much larger number of stations than competing topologies ( ShuffleNet and De Bruijn networks ) . Our experimental and analytical results show that despite its larger size ( more number of stations ) , a Kautz graph based network shows similar delay and throughput performance as a De Bruijn graph based network . This architecture provides the practicality for implementation . For example , with two transceivers per node , a 384-node ShuffleNet uses 96 fiber buses and 6 wavelengths . We conclude that the WDM mesh-based ShuffleNet is suited for high-speed local-area or metropolitan-area networks . We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently . We provide several example encoders including VGG16 , Resnet18 , MobileNet , and ShuffleNet and decoders including SkipNet , UNet and Dilation Frontend . The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks . Their performance in terms of update delay , processing complexity , failure tolerance and growth complexity is examined . Our results reveal that the ShuffleNet and hypercube scheme provide identical maximum update delay and similar processing complexity . However , as the number of nodes in the system changes ( e.g. , due to failure or temporary out of service for maintenance ) , the hypercube scheme maintains all existing synchronization sessions and greatly simplifies system administration overhead such as moving files from node to node for the purpose of data synchronization . It can obtain a surprisingly large number of sub-networks ( $ > 10^{19}$ ) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently . On diverse edge devices , OFA consistently outperforms state-of-the-art ( SOTA ) NAS methods ( up to 4.0 % ImageNet top1 accuracy improvement over MobileNetV3 , or same accuracy but 1.5x faster than MobileNetV3 , 2.6x faster than EfficientNet w.r.t measured latency ) while reducing many orders of magnitude GPU hours and $ CO_2 $ emission . In particular , OFA achieves a new SOTA 80.0 % ImageNet top-1 accuracy under the mobile setting ( $ < $ 600 M MACs ) . MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval . It builds on a standard classification trunk . The top of the network produces an embedding containing coarse and fine-grained information , so that images can be recognized based on the object class , particular object , or if they are distorted copies . Our joint training is simple : we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation , with no need for additional labels . A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution . When fed to a linear classifier , the learned embeddings provide state-of-the-art classification accuracy . document : MultiGrain : a unified image embedding for classes and instances MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval . In summary , our main contributions are as follows : We introduce the MultiGrain architecture , which outputs an image embedding incorporating different levels of granularity . Our dual classification + instance objective improves the classification accuracy on its own . As described in sec : input - size , we train our network on crops of size pixels . For testing , we experiment with computing MultiGrain embeddings at resolutions . For resolution , we follow the classical image classification protocol “ resolution 224 ” : the smallest side of an image is resized to and then a central crop is extracted . subsection : Classification results From now on , our MultiGrain nets are trained at resolution = with exponent = ( standard average pooling ) or = in the GeM pooling . tab : classifres presents the classification results . There is a large improvement in classification performance from our baseline Resnet - 50 with = , = , “ full ” data augmentation ( 76.2 % top - 1 accuracy ) , to a MultiGrain model at = , = , = ( 78.6 % top - 1 ) . We identify four sources for this improvement : Repeated augmentations : adding RA batch sampling ( sec : data - augmented - batches ) yields an improvement of . pooling : GeM at training ( sec : p - pooling ) allows the margin loss to have a much stronger effect thanks to increased localization of the features : . Expanding resolution : evaluating at resolution adds to the MultiGrain network , reaching the top - 1 accuracy . This is made possible by the training – which yields sparser features , more generalizable over different resolutions , and by the pooling adaptation – without it the performance at this resolution is only .
We evaluated micro and macro-F1 scores of four information extraction tasks , including subsite , laterality , behavior , and histological grades from cancer pathology reports . The scores for the Text GCN significantly outperformed our previous studies with convolutional neural networks , suggesting that the Text GCN model is superior to traditional models in task performance .
Two-stream architecture have shown strong performance in video classification task . The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally . However , there are some problems within such architecture .
Learning non-parametric invariances directly from data remains an important open problem . In this paper , we introduce a new architectural layer for convolutional networks which is capable of learning general invariances from data itself . This layer can learn invariance to non-parametric transformations and interestingly , motivates and incorporates permanent random connectomes , thereby being called Permanent Random Connectome Non-Parametric Transformation Networks ( PRC-NPTN ) .
The dataset contains RGB images from young carrot seedlings taken during the period of February in the area around Negotino , Republic of Macedonia . We performed initial analysis of the dataset and report the initial results , obtained using convolutional neural network architectures .
It was shown that the custom image processing pipeline , which achieved a worm segmentation accuracy of 94 % , was outperformed by all of the architectures considered , with the best being 97.3 % achieved by a FCN with a single downsampling layer . These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans .
These scenes have not been well-handled in computer vision tasks . Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers ; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches . In this paper , we proposed a new CNN learning approach to categorize skyline scenes .
Thus , we should expect early sensory processing to supply classification information fast and accurately , even before relying the information to higher brain areas or more complex system components downstream . Today , advanced convolutional artificial neural networks can successfully solve visual detection and classification tasks and are commonly used to build complex decision making systems . However , in order to perform well on these tasks they require increasingly complex , " very deep " model structure , which is costly in inference run-time , energy consumption and number of training samples , only trainable on cloud-computing clusters .
Luckily , current GPUs , paired with a highly - optimized implementation of 2D convolution , are powerful enough to facilitate the training of interestingly - large CNNs , and recent datasets such as ImageNet contain enough labeled examples to train such models without severe overfitting . The specific contributions of this paper are as follows : we trained one of the largest convolutional neural networks to date on the subsets of ImageNet used in the ILSVRC - 2010 and ILSVRC - 2012 competitions [ 2 ] and achieved by far the best results ever reported on these datasets . We wrote a highly - optimized GPU implementation of 2D convolution and all the other operations inherent in training convolutional neural networks , which we make available publicly1 .
document : Quaternion Convolutional Neural Networks for End - to - End Automatic Speech Recognition Recently , the connectionist temporal classification ( CTC ) model coupled with recurrent ( RNN ) or convolutional neural networks ( CNN ) , made it easier to train speech recognition systems in an end - to - end fashion . However in real - valued models , time frame components such as mel - filter - bank energies and the cepstral coefficients obtained from them , together with their first and second order derivatives , are processed as individual elements , while a natural alternative is to process such components as composed entities .
document : Deep Label Distribution Learning With Label Ambiguity Convolutional Neural Networks ( ConvNets ) have achieved excellent recognition performance in various visual recognition tasks . A large labeled training set is one of the most important factors for its success .
This section considers the final , global spatial pooling layer . Local pooling operators , usually max pooling , are found throughout the layers of most convolutional networks to achieve local invariance to small translations . By contrast , global spatial pooling converts a 3D tensor of activations produced by a convolutional trunk to a vector .
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models . In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image - dependent spatial models for the task of pose estimation . The contribution of this paper is to implicitly model long - range dependencies between variables in structured prediction tasks such as articulated pose estimation .
There have been several attempts to use convolutional encoder models for neural machine trans - lation in the past but they were either only applied to rescoring n - best lists of classical systems [ reference ] or were not competitive to recurrent alternatives [ reference ] . This is despite several attractive properties of convolutional networks . For example , convolutional networks operate over a fixed - size window of the input sequence which enables the simultaneous computation of all features for a source sentence .
At the first level , each cluster of users can be connected either via a ShuffleNet ( SH ) or a de Bruijn network ( dB ) . At the second level , all the clusters in the system can be connected by two rings in opposite directions ( SH/Ring and dB/Ring ) , or a de Bruijn network ( dB/dB ) , or a ShuffleNet ( SH/SH ) . The performances of the ShuffleNet , the de Bruijn networks , and the hierarchical networks : SH/Ring , dB/Ring , dB/dB and SH/SH , are compared .
The loop-augmented ShuffleNet is constructed with slight change in the ShuffleNet connectivity while retaining the simple addressing and self-routing properties . Compared with ShuffleNet , the network performance is greatly improved by reducing the expected number of hops using this new network design .
In a multi-star network , however , one does not have the same degree of freedom for reconfiguration because one is not free to exchange the logical identities of an arbitrary pair of nodes . In the paper , the authors investigate the reconfigurability issue in the multi-star implementation of ShuffleNet . They show how one can attain maximum reconfigurability by properly choosing the system parameters .
Modular growth of ShuffleNets , however , is generally difficult and requires many hardware and software reconfigurations . The authors consider a multi-star implementation of ShuffleNet and discuss how a ( p , k ) ShuffleNet can be expanded to a ( p , k+1 ) ShuffleNet in modular phases , where each phase increases the number of nodes by only a small fraction and requires only minor hardware and software reconfigurations.<<ETX > >
Moreover , YOLOv3 was improved to obtain more effective detection results . First , we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2 . Second , the prediction box center point , XY loss , and prediction box width and height , WH loss , in the loss function was replaced with the generalized intersection over union ( GIoU ) loss .
Logical topologies proposed in the literature include the ShuffleNet , De Bruijn graph , Manhattan Street Network , and Hypercube . We show that for a given nodal in-degree and out-degree and average number of hops between stations , a network based on Kautz graphs can support a much larger number of stations than competing topologies ( ShuffleNet and De Bruijn networks ) . Our experimental and analytical results show that despite its larger size ( more number of stations ) , a Kautz graph based network shows similar delay and throughput performance as a De Bruijn graph based network .
This architecture provides the practicality for implementation . For example , with two transceivers per node , a 384-node ShuffleNet uses 96 fiber buses and 6 wavelengths . We conclude that the WDM mesh-based ShuffleNet is suited for high-speed local-area or metropolitan-area networks .
We implemented a generic meta-architecture via a decoupled design where different types of encoders and decoders can be plugged in independently . We provide several example encoders including VGG16 , Resnet18 , MobileNet , and ShuffleNet and decoders including SkipNet , UNet and Dilation Frontend . The framework is scalable for addition of new encoders and decoders developed in the community for other vision tasks .
Their performance in terms of update delay , processing complexity , failure tolerance and growth complexity is examined . Our results reveal that the ShuffleNet and hypercube scheme provide identical maximum update delay and similar processing complexity . However , as the number of nodes in the system changes ( e.g. , due to failure or temporary out of service for maintenance ) , the hypercube scheme maintains all existing synchronization sessions and greatly simplifies system administration overhead such as moving files from node to node for the purpose of data synchronization .
It can obtain a surprisingly large number of sub-networks ( $ > 10^{19}$ ) that can fit different hardware platforms and latency constraints while maintaining the same level of accuracy as training independently . On diverse edge devices , OFA consistently outperforms state-of-the-art ( SOTA ) NAS methods ( up to 4.0 % ImageNet top1 accuracy improvement over MobileNetV3 , or same accuracy but 1.5x faster than MobileNetV3 , 2.6x faster than EfficientNet w.r.t measured latency ) while reducing many orders of magnitude GPU hours and $ CO_2 $ emission . In particular , OFA achieves a new SOTA 80.0 % ImageNet top-1 accuracy under the mobile setting ( $ < $ 600 M MACs ) .
MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval . It builds on a standard classification trunk . The top of the network produces an embedding containing coarse and fine-grained information , so that images can be recognized based on the object class , particular object , or if they are distorted copies . Our joint training is simple : we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation , with no need for additional labels . A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution . When fed to a linear classifier , the learned embeddings provide state-of-the-art classification accuracy .
document : MultiGrain : a unified image embedding for classes and instances MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval .
In summary , our main contributions are as follows : We introduce the MultiGrain architecture , which outputs an image embedding incorporating different levels of granularity . Our dual classification + instance objective improves the classification accuracy on its own .
As described in sec : input - size , we train our network on crops of size pixels . For testing , we experiment with computing MultiGrain embeddings at resolutions . For resolution , we follow the classical image classification protocol “ resolution 224 ” : the smallest side of an image is resized to and then a central crop is extracted .
subsection : Classification results From now on , our MultiGrain nets are trained at resolution = with exponent = ( standard average pooling ) or = in the GeM pooling .
tab : classifres presents the classification results . There is a large improvement in classification performance from our baseline Resnet - 50 with = , = , “ full ” data augmentation ( 76.2 % top - 1 accuracy ) , to a MultiGrain model at = , = , = ( 78.6 % top - 1 ) . We identify four sources for this improvement : Repeated augmentations : adding RA batch sampling ( sec : data - augmented - batches ) yields an improvement of .
pooling : GeM at training ( sec : p - pooling ) allows the margin loss to have a much stronger effect thanks to increased localization of the features : . Expanding resolution : evaluating at resolution adds to the MultiGrain network , reaching the top - 1 accuracy . This is made possible by the training – which yields sparser features , more generalizable over different resolutions , and by the pooling adaptation – without it the performance at this resolution is only .
Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification Learning Non-Parametric Invariances from Data with Permanent Random Connectomes Weed Detection Dataset with RGB Images Taken Under Variable Light Conditions Semantic segmentation of bioimages using convolutional neural networks Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding Reliable counting of weakly labeled concepts by a single spiking neuron model ImageNet classification with deep convolutional neural networks Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition Deep Label Distribution Learning With Label Ambiguity MultiGrain: a unified image embedding for classes and instances Convolutional Pose Machines A Convolutional Encoder Model for Neural Machine Translation Hierarchical networks: for optical communications Contention resolution in the loop-augmented ShuffleNet multihop lightwave network Reconfigurability of ShuffleNets in multi-star implementation Multi-star implementation of expandable ShuffleNets Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3 Kautz graphs as attractive logical topologies in multihop lightwave networks ShuffleNet=hypercube/spl times/ring and embedding ShuffleNet on mesh network A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving Data Synchronization Methods Based on ShuffleNet and Hypercube for Networked Information Systems Once for All: Train One Network and Specialize it for Efficient Deployment MultiGrain: a unified image embedding for classes and instances MultiGrain: a unified image embedding for classes and instances
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BigData AAAI ICT Innovations IJCNN CACM INTERSPEECH IEEE Transactions on Image Processing ArXiv 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACL INFOCOM CVPR Workshops INFOCOM ArXiv
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