Classification is based on the cytology shown by vaginal smear . For this reason , we use manual and automatic feature extraction ; these features are classified with support vector machines , multilayer perceptron networks and convolutional neural networks . A dataset of 412 images of estrous cycle was used . We present an approach to low-level vision that combines two main ideas : the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models . We demonstrate this approach on the challenging problem of natural image denoising . In this paper , several convolutional neural network architectures are investigated for online isolated handwritten character recognition ( Latin alphabet ) . Two main architectures have been developed and optimised . This paper aims to identify individual lobsters based on the patterns on their exoskeleton . Convolutional neural network architectures have been identified as an effective tool towards that , specifically siamese networks which were used to identify unique lobsters with a top-3 accuracy of about 84 % . Building discriminative representations for 3D data has been an important task in computer graphics and computer vision research . Convolutional Neural Networks ( CNNs ) have shown to operate on 2D images with great success for a variety of tasks . Lifting convolution operators to 3D ( 3DCNNs ) seems like a plausible and promising next step . combines a convolutional network part - detector with a part - based spatial model into a unified framework . Because human pose estimation is ultimately a structured prediction task , it is difficult for convolutional networks to correctly regress the full pose in a single pass . Recently , iterative refinement techniques have been proposed to address this issue . For the LSTM system , the conversation - side i - vector is appended to each frame of input . For convolutional networks , this approach is inappropriate because we do not expect to see spatially contiguous patterns in the input . Instead , for the CNNs , we add a learnable weight matrix to each layer , and add to the activation of the layer before the nonlinearity . To avoid relying on mass labelled data , the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained . Consequently , in the proposed method , the first five convolutional layers of AlexNet are utilized for general feature learning ; then three subsequent layers are designed and fine-tuned for the classification of fundus images . The best performance for cataract detection is 95.37 % in terms of classification accuracy . This paper proposed a high performance method for vegetable images classification based on deep learning framework . The AlexNet network model in Caffe was used to train the vegetable image data set . The vegetable image data set was obtained from ImageNet and divided into training data set and test data set . This paper puts forward an advanced AlexNet network model combined with Harris feature detection , which guides the image processing of the original model according to the detected Harris characteristics , it reduces the randomness error and improves the generalization ability and robustness of the model . In addition , for the campus indoor scene positioning environment , the original structure of the AlexNet network model and the data augmentation method are improved , so that the positioning model can cope with the complex and variable positioning environment , and its accuracy and speed reach a high level . The method can be combined with the existing mainstream visual indoor positioning method to enhance the accurateness and speed of positioning system . In this study , we investigate three class skin lesion classification problem of a low quality and small size dataset using transfer learning using AlexNet deep Convolutional Neural Network ( CNN ) . Our approach involves modifying the pre-trained AlexNet model ; through replacing the decision layer to be compatible with our three class problem . In addition , we propose adding two dropout layers to overcome the over fitting problem . Heuristic energy map is then used to distinguish the license plate area . To validate the detected regions , high-level features extracted from AlexNet Convolutional Neural Network are used . Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance , but also outperforms the conventional and deep-learning methods . The selected keyframes are used to compute the optical flow values of magnitudes , orientations , and velocities of each keyframe to generate several 2D templates . Then , the obtained 2D templates are supplied to a pre-trained model ‘ AlexNet ’ to extract high-level features . The Neighborhood Component Analysis ( NCA ) feature selection method is applied to select the appropriate features , then use these features to generate a classification model via Support Vector Machine ( SVM ) classifier . The ubiquitous approach of transfer learning for feature extraction is harnessed for image based detection of two types of craniofacial abnormalities : pediatric cleft and craniosynostosis . In the current study , using features extracted from pre-trained AlexNet activations , we train a multi class support vector machine ( SVM ) for cleft lip abnormality and developed a multi-view classifier using max voting for craniosynostosis anomaly detection . We achieved Area under the ROC curve ( AUC ) value of 0.95 for cleft abnormality and 0.84 for craniosynostosis . We have obtained percentage classification rates in the high 90s , and our results show there is little difference between SVM and transfer learning . Thus , the present study show that an AlexNet driven CNN with SVM and fine-tuned classifiers are a suitable choice for accurate discrimination between tumour and non-tumour histological samples from the head and neck . As a dataset , an open dataset gathered by the Laboratory of Electronics and Imaging of the National Center for Scientific Research in Chalon-sur-Saone was used . The architecture of the CNN AlexNet , which was used as a starting point for the classifier , was adapted to solve fall detection problem . The proposed method was tested on a dataset of 30 records containing a single fall episode each . We evaluate our approach on the ImageNet classification task . The classification accuracy with a Binary-Weight-Network version of AlexNet is the same as the full-precision AlexNet . We compare our method with recent network binarization methods , BinaryConnect and BinaryNets , and outperform these methods by large margins on ImageNet , more than \(16\,\%\ ) in top-1 accuracy . The FMM reduced energy and total flow by approximately 17.86 % and 34.16 % , respectively , using different traffic patterns . Thus , the FMM improved the performance of AlexNet traffic distribution while the impact on data flow reduced energy consumption . After the popular CNN model AlexNet was proposed , state - of - the - art detection shifted towards CNNs for feature extraction . Girshick et al . developed R - CNN , a two part system which used Selective Search to propose regions and AlexNet to classify them . R - CNN achieved state - of - the - art on Pascal by a large margin ; however , due to its nearly classification queries and inefficient re - use of convolutions , it remains impractical for real - time implementations . ( fig . 1 ) . Our method ( section 3 ) starts from an existing network , such as AlexNet pre - trained on ImageNet data , and extends it to reason explicitly and efficiently about image regions R. In order to do so , given an image x , the first step is to efficiently extract region - level descriptors φ ( x ; R ) by inserting a spatial pyramid pooling layer on top of the convolutional layers of the CNN Hyperface and All - In - One both use a single network for numerous facial analysis tasks . Hyperface uses an AlexNet pre - trained on ImageNet as a backbone and All - In - One uses a backbone b ) But more importantly , they did not see real effect of data due to use of smaller AlexNet of VGG models . In our experiments , we see more gain with larger model sizes . As such , the VGGNet provides a stronger multi - scale representation model than AlexNet , with fewer parameters . However , both AlexNet and VGGNet stack filters in a linear topology , which means these networks can only have a relatively inflexible receptive fields and are optimal at handling objects with a small range of scales . The GoogLeNet utilizes parallel filters with different kernel sizes to enhance the multi - scale representation capability . Deepening feed - forward neural networks has generally returned dividends in performance . A striking example within the computer vision community is the improvement on the ImageNet deng2009imagenet classification task when transitioning from AlexNet alexNet12 to VGG vgg16 to GoogLeNet szegedy2015inception to ResNet he2015deep . Unfortunately , greater depth also makes training more challenging , at least when employing a first - order optimization method with randomly initialized layers . We choose the VGG - 16 network architecture . Compared with AlexNet variants , it offers better accuracy for most visual recognition tasks , but is also slower and more memory demanding . We use the VGG - 16 model parameters released by the authors , which was trained on 1 , 000 ImageNet categories . section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks . In this section , we test if CBS schedules can alleviate the problem of sub - optimal initialization . We test a Gaussian initialization with mean 0 and standard deviation 0.1 on an AlexNet - like model ( C1 ) . The baseline ( BL ) training follows the same setting as described in Appendix A and achieves final accuracy 84.27 % . — NiN - style , DSN - style , the 50 M parameter AlexNet , and the 22 - layer GoogLeNet . Acknowledgment This work is supported by NSF awards IIS - 1216528 ( IIS - 1360566 ) and IIS - 0844566 ( IIS - 1360568 ) . tableDeep Network architecture details The top three convolutional layers ( conv1 - conv3 ) are initialized with the weights from the AlexNet model trained on the ImageNet challenge dataset . Several recent works ( , ) have empirically shown that this transfer of knowledge across different networks , albeit for a different objective , improves performance and more significantly reduces the need to train over a large number of iterations . Technoethical inquiry deals with a variety of social , legal , cultural , economic , political , and ethical implications of new technological applications which can threaten important aspects of contemporary life and society . GhostNet is a large-scale cyber espionage network which has infiltrated important political , economic , and media institutions including embassies , foreign ministries and other government offices in 103 countries and infected at least 1,295 computers . The following case study explores the influences of GhostNet on affected organizations by critically reviewing GhostNet documentation and relevant literature on cyber espionage . Ghost bottlenecks are designed to stack Ghost modules , and then the lightweight GhostNet can be easily established . Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models , and our GhostNet can achieve higher recognition performance ( e.g. 75.7 % top-1 accuracy ) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset . Code is available at https://github.com/huawei-noah/ghostnet .
Classification is based on the cytology shown by vaginal smear . For this reason , we use manual and automatic feature extraction ; these features are classified with support vector machines , multilayer perceptron networks and convolutional neural networks . A dataset of 412 images of estrous cycle was used .
We present an approach to low-level vision that combines two main ideas : the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models . We demonstrate this approach on the challenging problem of natural image denoising .
In this paper , several convolutional neural network architectures are investigated for online isolated handwritten character recognition ( Latin alphabet ) . Two main architectures have been developed and optimised .
This paper aims to identify individual lobsters based on the patterns on their exoskeleton . Convolutional neural network architectures have been identified as an effective tool towards that , specifically siamese networks which were used to identify unique lobsters with a top-3 accuracy of about 84 % .
Building discriminative representations for 3D data has been an important task in computer graphics and computer vision research . Convolutional Neural Networks ( CNNs ) have shown to operate on 2D images with great success for a variety of tasks . Lifting convolution operators to 3D ( 3DCNNs ) seems like a plausible and promising next step .
combines a convolutional network part - detector with a part - based spatial model into a unified framework . Because human pose estimation is ultimately a structured prediction task , it is difficult for convolutional networks to correctly regress the full pose in a single pass . Recently , iterative refinement techniques have been proposed to address this issue .
For the LSTM system , the conversation - side i - vector is appended to each frame of input . For convolutional networks , this approach is inappropriate because we do not expect to see spatially contiguous patterns in the input . Instead , for the CNNs , we add a learnable weight matrix to each layer , and add to the activation of the layer before the nonlinearity .
To avoid relying on mass labelled data , the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained . Consequently , in the proposed method , the first five convolutional layers of AlexNet are utilized for general feature learning ; then three subsequent layers are designed and fine-tuned for the classification of fundus images . The best performance for cataract detection is 95.37 % in terms of classification accuracy .
This paper proposed a high performance method for vegetable images classification based on deep learning framework . The AlexNet network model in Caffe was used to train the vegetable image data set . The vegetable image data set was obtained from ImageNet and divided into training data set and test data set .
This paper puts forward an advanced AlexNet network model combined with Harris feature detection , which guides the image processing of the original model according to the detected Harris characteristics , it reduces the randomness error and improves the generalization ability and robustness of the model . In addition , for the campus indoor scene positioning environment , the original structure of the AlexNet network model and the data augmentation method are improved , so that the positioning model can cope with the complex and variable positioning environment , and its accuracy and speed reach a high level . The method can be combined with the existing mainstream visual indoor positioning method to enhance the accurateness and speed of positioning system .
In this study , we investigate three class skin lesion classification problem of a low quality and small size dataset using transfer learning using AlexNet deep Convolutional Neural Network ( CNN ) . Our approach involves modifying the pre-trained AlexNet model ; through replacing the decision layer to be compatible with our three class problem . In addition , we propose adding two dropout layers to overcome the over fitting problem .
Heuristic energy map is then used to distinguish the license plate area . To validate the detected regions , high-level features extracted from AlexNet Convolutional Neural Network are used . Extensive experiments on the license plates of six countries show that the proposed approach not only ensures real-time performance , but also outperforms the conventional and deep-learning methods .
The selected keyframes are used to compute the optical flow values of magnitudes , orientations , and velocities of each keyframe to generate several 2D templates . Then , the obtained 2D templates are supplied to a pre-trained model ‘ AlexNet ’ to extract high-level features . The Neighborhood Component Analysis ( NCA ) feature selection method is applied to select the appropriate features , then use these features to generate a classification model via Support Vector Machine ( SVM ) classifier .
The ubiquitous approach of transfer learning for feature extraction is harnessed for image based detection of two types of craniofacial abnormalities : pediatric cleft and craniosynostosis . In the current study , using features extracted from pre-trained AlexNet activations , we train a multi class support vector machine ( SVM ) for cleft lip abnormality and developed a multi-view classifier using max voting for craniosynostosis anomaly detection . We achieved Area under the ROC curve ( AUC ) value of 0.95 for cleft abnormality and 0.84 for craniosynostosis .
We have obtained percentage classification rates in the high 90s , and our results show there is little difference between SVM and transfer learning . Thus , the present study show that an AlexNet driven CNN with SVM and fine-tuned classifiers are a suitable choice for accurate discrimination between tumour and non-tumour histological samples from the head and neck .
As a dataset , an open dataset gathered by the Laboratory of Electronics and Imaging of the National Center for Scientific Research in Chalon-sur-Saone was used . The architecture of the CNN AlexNet , which was used as a starting point for the classifier , was adapted to solve fall detection problem . The proposed method was tested on a dataset of 30 records containing a single fall episode each .
We evaluate our approach on the ImageNet classification task . The classification accuracy with a Binary-Weight-Network version of AlexNet is the same as the full-precision AlexNet . We compare our method with recent network binarization methods , BinaryConnect and BinaryNets , and outperform these methods by large margins on ImageNet , more than \(16\,\%\ ) in top-1 accuracy .
The FMM reduced energy and total flow by approximately 17.86 % and 34.16 % , respectively , using different traffic patterns . Thus , the FMM improved the performance of AlexNet traffic distribution while the impact on data flow reduced energy consumption .
After the popular CNN model AlexNet was proposed , state - of - the - art detection shifted towards CNNs for feature extraction . Girshick et al . developed R - CNN , a two part system which used Selective Search to propose regions and AlexNet to classify them . R - CNN achieved state - of - the - art on Pascal by a large margin ; however , due to its nearly classification queries and inefficient re - use of convolutions , it remains impractical for real - time implementations .
( fig . 1 ) . Our method ( section 3 ) starts from an existing network , such as AlexNet pre - trained on ImageNet data , and extends it to reason explicitly and efficiently about image regions R. In order to do so , given an image x , the first step is to efficiently extract region - level descriptors φ ( x ; R ) by inserting a spatial pyramid pooling layer on top of the convolutional layers of the CNN
Hyperface and All - In - One both use a single network for numerous facial analysis tasks . Hyperface uses an AlexNet pre - trained on ImageNet as a backbone and All - In - One uses a backbone
b ) But more importantly , they did not see real effect of data due to use of smaller AlexNet of VGG models . In our experiments , we see more gain with larger model sizes .
As such , the VGGNet provides a stronger multi - scale representation model than AlexNet , with fewer parameters . However , both AlexNet and VGGNet stack filters in a linear topology , which means these networks can only have a relatively inflexible receptive fields and are optimal at handling objects with a small range of scales . The GoogLeNet utilizes parallel filters with different kernel sizes to enhance the multi - scale representation capability .
Deepening feed - forward neural networks has generally returned dividends in performance . A striking example within the computer vision community is the improvement on the ImageNet deng2009imagenet classification task when transitioning from AlexNet alexNet12 to VGG vgg16 to GoogLeNet szegedy2015inception to ResNet he2015deep . Unfortunately , greater depth also makes training more challenging , at least when employing a first - order optimization method with randomly initialized layers .
We choose the VGG - 16 network architecture . Compared with AlexNet variants , it offers better accuracy for most visual recognition tasks , but is also slower and more memory demanding . We use the VGG - 16 model parameters released by the authors , which was trained on 1 , 000 ImageNet categories .
section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks .
In this section , we test if CBS schedules can alleviate the problem of sub - optimal initialization . We test a Gaussian initialization with mean 0 and standard deviation 0.1 on an AlexNet - like model ( C1 ) . The baseline ( BL ) training follows the same setting as described in Appendix A and achieves final accuracy 84.27 % .
— NiN - style , DSN - style , the 50 M parameter AlexNet , and the 22 - layer GoogLeNet . Acknowledgment This work is supported by NSF awards IIS - 1216528 ( IIS - 1360566 ) and IIS - 0844566 ( IIS - 1360568 ) .
tableDeep Network architecture details The top three convolutional layers ( conv1 - conv3 ) are initialized with the weights from the AlexNet model trained on the ImageNet challenge dataset . Several recent works ( , ) have empirically shown that this transfer of knowledge across different networks , albeit for a different objective , improves performance and more significantly reduces the need to train over a large number of iterations .
Technoethical inquiry deals with a variety of social , legal , cultural , economic , political , and ethical implications of new technological applications which can threaten important aspects of contemporary life and society . GhostNet is a large-scale cyber espionage network which has infiltrated important political , economic , and media institutions including embassies , foreign ministries and other government offices in 103 countries and infected at least 1,295 computers . The following case study explores the influences of GhostNet on affected organizations by critically reviewing GhostNet documentation and relevant literature on cyber espionage .
Ghost bottlenecks are designed to stack Ghost modules , and then the lightweight GhostNet can be easily established . Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models , and our GhostNet can achieve higher recognition performance ( e.g. 75.7 % top-1 accuracy ) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset . Code is available at https://github.com/huawei-noah/ghostnet .
Estrous Cycle Classification through Automatic Feature Extraction Natural Image Denoising with Convolutional Networks Multi-modular architecture based on convolutional neural networks for online handwritten character recognition Visual fingerprinting for lobsters using deep learning FPNN: Field Probing Neural Networks for 3D Data Towards Viewpoint Invariant 3D Human Pose Estimation Achieving Human Parity in Conversational Speech Recognition Automatic Classification of Cataract based on Deep Learnig High performance vegetable classification from images based on AlexNet deep learning model Research on Indoor Positioning Method Based on Improved HS-AlexNet Model Low quality dermal image classification using transfer learning License plate detection for multi-national vehicles: An illumination invariant approach in multi-lane environment Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection Image Based Detection of Craniofacial Abnormalities using Feature Extraction by Classical Convolutional Neural Network A Deep Learning Approach to Tumour Identification in Fresh Frozen Tissues Machine learning in video surveillance for fall detection XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks Flow mapping on mesh-based deep learning accelerator An Empirical Evaluation of Deep Learning on Highway Driving Weakly Supervised Deep Detection Networks Fine-Grained Head Pose Estimation Without Keypoints Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Res2Net: A New Multi-scale Backbone Architecture FractalNet: Ultra-Deep Neural Networks without Residuals ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks Rethinking the Inception Architecture for Computer Vision Parameter Re-Initialization through Cyclical Batch Size Schedules Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Triplet probabilistic embedding for face verification and clustering Socio-Technical Influences of Cyber Espionage: A Case Study of the GhostNet System GhostNet: More Features From Cheap Operations
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