Reading the text embedded in natural scene images is essential to many applications . In this paper , we propose a method for detecting text in scene images based on multi-level connected component ( CC ) analysis and learning text component features via convolutional neural networks ( CNN ) , followed by a graph-based grouping of overlapping text boxes . The multi-level CC analysis allows the extraction of redundant text and non-text components at multiple binarization levels to minimize the loss of any potential text candidates . Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases , without considering the topological structure information among these nodes , which is important for predicting lncRNA – disease associations . We propose a method based on information flow propagation and convolutional neural networks , called LDAPred , to predict disease-related lncRNAs . LDAPred not only integrates the similarities , associations , and interactions among lncRNAs , diseases , and miRNAs , but also exploits the topological structures formed by them . We propose a powerful adaptation of the state-of-the-art Speech Recognition models for these tasks and demonstrate the effectiveness of our techniques on standard datasets . Our architectures are a combination of Bidirectional Long Short Term Memory ( LSTM ) Networks , Convolutional Networks , and Fully Connected Networks , trained by Gradient Descent to minimize the Cross Entropy and the Connectionist Temporal Classification ( CTC ) losses . We adapt the Libri Speech corpus for the task of segmentation and diarization . In this paper , we study such features obtained from a compact short-range 60-GHz frequency modulated continuous wave radar for various human activities . We present novel Convolutional Neural Network architectures to leverage these features for human activity classification . Tree - structured models treat each word token as a node in a syntactic parse tree and learn sentence representations from leaves to the root in a recursive manner . Convolutional neural networks ( CNNs ) and recurrent neural networks ( RNNs ) have emerged as two widely used architectures and are often combined with sequence - based or tree - structured models . Owing to the capability of capturing local correlations of spatial or temporal structures , CNNs have achieved top performance in computer vision , speech recognition and NLP . For our experiments we resized all images to pixels . appendix : Convolutional Neural Networks Our models for CIFAR - 10 and ImageNet are deep , feed - forward convolutional neural networks ( CNNs ) . CNN : NIPS and CNN : NAACL apply convolutional neural networks ( CNNs ) as the individual sentence model , where a set of feature detectors over successive words are designed to extract local features . LSTM : Compared to the 2013 result , the accuracy has almost doubled . The top performing teams all use Convolutional Networks . We report the official scores in Table [ reference ] and common strategies for each team : the use of external data , ensemble models or contextual models . However , there has been no systematic research on the predictive power and configurations of different deep neural networks in this task . In this work , we systematically explore the configurations and performances of three adapted deep neural networks : ResNet , LSTM , and hybrid ResNet-LSTM . Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset . The proposed network exploits the rich information extracted from the transform domain to reconstruct more accurate high-resolution images . In addition , to reap the benefits from both residual network ( ResNet ) and densely convolutional network ( DenseNet ) topologies , we use dual-path blocks as the basic building blocks which allow feature re-use while ensuring the ability to continue extracting new features . Thanks to extensive research on the attention mechanism , we further introduce spatial and self-attention blocks to refine features based on feature correlations at different layers . In this article , we collected and labeled a dataset containing 9 types of ground weather images . In addition , we use ResNet convolutional neural network and DenseNet convolutional neural network to build the network structure , and perform probability discrimination on the output results of each model to increase the recognition rate . We trained and tested on the collected weather dataset . Based on the shape features of pieces , an anchor mechanism is designed to detect pieces . The method of extracting eight-direction features and rotating invariant HOG features are used to train the ResNet convolutional neural network , which can improve the problem of fewer types of training-intensive chess pieces , single rotating pictures , etc . Experimental results show that the proposed convolutional neural network for chess detection and recognition can greatly enhance the detection of environmental adaptability , robustness , and improve the detection speed and the recognition accuracy . Thus , this study proposes a HS reconstruction , which fuses merely two RGB images with redundant spectral responses . In this work , we design a new RGB camera via shifting the SS of an existed RGB camera , which can provide similar strength of spectral response with different spectral centers of SS , and fuse the new achieved color image with an existed RGB image by a deep ResNet . Experiments validate that fusion of two existed RGB images can provide impressive HS reconstruction performance and further improvement can be achieved by integrating the color image of the simulated SS with the RGB image . In terms of the datasets , a high sample-identity ratio is conducive to generalization , but it leads to increased difficulty for the training to converge . For the network architecture , deep ResNet has an advantage over other designs . Various normalization operations in the network are also necessary . Methods CEUS images of 527 patients over three phases ( un-enhanced , early arterial and late portal phases ) were manually pre-processed into square frames . Supervised CNN was designed as the 152-layer ResNet trained on each phase frames separately and assembled by SVM algorithm . The model was trained on 55350 augmented frames and tested on 474 un-augmented frames . We evaluate the quality and diversity of generated samples with the robust metric Fréchet Inception Distance , a measure that can detect the intra-class mode dropping and is consistent with human assessment . In addition , we conduct a battery of experiments with the widely used architecture DCGAN and the complex architecture ResNet to demonstrate the effectiveness and robustness of the proposed method . The method introduced in this paper aims at helping computer vision practitioners faced with an overfit problem . The idea is to replace , in a 3-branch ResNet , the standard summation of residual branches by a stochastic affine combination . The largest tested model improves on the best single shot published result on CIFAR10 by reaching 2.86 % test error . Second , for computational neuroscience , CNNs provide a testable modelling platform for visual categorizations inspired by the multi-layered organization of visual cortex [ 7 ] . Here , we used a 3D generative model to control the variance of information learned to identify 2,000 face identities in one CNN architecture ( 10-layer ResNet [ 9 ] ) . We generated 25 M face images to train the network by randomly sampling intrinsic ( i.e. face morphology , gender , age , expression and ethnicity ) and extrinsic factors of face variance ( i.e. 3D pose , illumination , scale and 2D translation ) . To address this , we propose an end-to-end deep blind synthesized image quality metric SIQA-CFP . We particularly design a contextual multilevel feature pooling module to encode low- and high-level features , which are extracted by a deep pre-trained ResNet . Experimental results on IRCCyN/IVC DIBR dataset show that our method outperforms state-of-the-art synthesized image quality metrics . A novel cascade learning framework to incrementally train deeper and more accurate convolutional neural networks is introduced . The proposed cascade learning facilitates the training of deep efficient networks with plain convolutional neural network ( CNN ) architectures , as well as with residual network ( ResNet ) architectures . This is demonstrated on the problem of image super-resolution ( SR ) . Our newly collected Naver Maps ' Street View dataset is used for training regression ResNet-34 and for comparison with previous methods . It is concluded that the trained regression ResNet outperforms previous methods in terms of both computation time and accuracy . U - Nets are designed based on the residual modules . More recently , the DenseNet outperforms the ResNet in the image classification task , benefitting from its dense connections . We would like to use the dense connectivity into multiple U - Nets . The central idea is that using deeper networks should not only lead to improvements in classification but also object detection . Some recent work in this direction include ResNet , Inception - ResNet and ResNetXt for object detection . The second area of research has been to use contextual reasoning , proxy tasks for reasoning and other top - down mechanisms for improving representations for object detection . Existing approaches , such as the DeepDocClassifier , apply standard Convolutional Network architectures with transfer learning from the object recognition domain . The contribution of the paper is threefold : First , it investigates recently introduced very deep neural network architectures ( GoogLeNet , VGG , ResNet ) using transfer learning ( from real images ) . Second , it proposes transfer learning from a huge set of document images , i.e. documents . Tab . [ reference ] summarizes impact of different schemes on iSQRT - COV network with ResNet - 50 architecture . Without post - compensation , iSQRT - COV network fails to converge . We resize all images to resolution 256 × 512 for computational efficiency . As a shared representation function ( encoder ) , we use the ResNet - 50 architecture [ reference ] in fully - convolutional fashion . We take the ResNet - 50 architecture and only use layers prior to average pooling that are fully convolutional . In the testing phase , we abandon this auxiliary branch and only use the well optimized master branch for final prediction . This kind of deeply supervised training strategy for ResNet - based FCN is broadly useful under different experimental settings and works with the pre - trained ResNet model . This manifests the generality of such a learning strategy . The global pyramid pooling feature provides additional contextual information . We have also provided a deeply supervised optimization strategy for ResNet - based FCN network . We hope the implementation details publicly available can help the community adopt these useful strategies for scene parsing and semantic segmentation and advance related techniques . Residual networks he2015deep , or ResNets , lead a recent and dramatic increase in both depth and accuracy of convolutional neural networks , facilitated by constraining the network to learn residuals . ResNet variants he2015deep , he2016identity , huang2016stochasticdepth and related architectures In the following we describe our findings w.r.t . the different ResNet block architectures and also analyze the performance of our proposed wide residual networks . We note that for all experiments related to ¡ ¡ type of convolutions in a block ¿ ¿ and ¡ ¡ number of convolutions per block subsection : Convolutional Neural Network The ResNet - 50 model is used in our experiment . We resize the generated images to using bilinear sampling . To evaluate the performance of CORAL for age estimation from face images , we chose the ResNet - 34 architecture , which is a modern CNN architecture that is known for achieving good performance on a variety of image classification tasks . For the remainder of this paper , we refer to the original ResNet - 34 CNN with cross entropy loss as CE - CNN . To implement CORAL , we replaced the last output layer with the corresponding binary tasks ( Figure [ reference ] ) and refer to this CNN as CORAL - CNN . The pre - activation version reaches slightly higher training loss at convergence , but produces lower test error . This phenomenon is observed on ResNet - 110 , ResNet - 110 ( 1 - layer ) , and ResNet - 164 on both CIFAR - 10 and 100 . This is presumably caused by BN ’s regularization effect . Firstly , we decompose the original signal into low frequency components and high frequency components using wavelet packet analysis and we construct a energy model of the cell voltage signal for the calculation of the proportion of high frequency fault energy consumption of the original voltage signal . Then we build a dataset of low frequency component samples of cell voltage signal and we train a residual network model on this dataset for the identification of the fluctuations of low-frequency component of cell voltage . Finally , some expert rules are developed to determine the state of cell voltage . Abstract A non-intrusive sequence to sequence load disaggregation method based on deep delated convolution residual network is proposed in this work . Firstly , the original power data is normalized , and then the sliding window used to create the input for the residual network . The dilated convolution residual network model reduces the difficulty of network optimization and solves the problem of vanishing gradient . Based on conventional beamforming maps , the proposed method aims at estimating the source coordinates and the strength . The residual network architecture , a well-established model in the field of image recognition , is successfully applied to this task . The investigation reveals a method that fast and accurately renders the position and strength of an unknown source . The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions . Compared to the traditional convolutional neural network , the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters . The residual connection can solve the representational bottleneck problem of the features in the model . ResultsOur model achieves F1-score of 77.80 % on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches . ConclusionThe residual network-based model can reduce the negative impact of corpus noise to parameter learning , and the combination of character position attention mechanism will enhance the identification features of different type of entities . section : Addendum : Shortcut vs. Residual In later experiments , we found that a residual connection can achieve similar accuracies with fewer number of parameters , compared to a shortcut connection . Therefore , in order to reduce the model size and to also follow the SNLI leader - board settings ( e.g. , 300D and 600D embeddings ) , we performed some additional SNLI experiments with the shortcut connections replaced with residual connections , where the input to each next biLSTM layer is the concatenation of the word embedding and the summation of outputs of all previous layers ( related to ResNet in computer vision ) . The first is a multi - head self - attention mechanism , and the second is a simple , position - wise fully connected feed - forward network . We employ a residual connection he2016deep around each of the two sub - layers , followed by layer normalization . The decoder contains two LSTM layers . Residual connection He2016DeepRL with a scaling factor of is applied to the two decoder states to compute the outputs . subsection : Training on JFT - 300 M Data Although there are several novel ConvNet architectures recently proposed , we decide to use a standard Residual Network architecture with 101 layers ( ResNet - 101 ) for its state - of - the - art performance and the ease of comparison with previous work . To train a ResNet - 101 model on JFT - 300 M , We add a fully - connected layer with 18291 outputs at the end of the network for classification . section : B. Baseline Evaluation In Section 5.2 of the main paper , we describe the setting of our baseline method ( Residual Network Baseline ) and compare it to our FRRN A network . To emphasize on a proper training procedure of both baselines , Figure 9 shows the mean IoU score on the validation set over time . Table [ reference ] shows that FractalNet scales to ImageNet , matching ResNet he2015deep at equal depth . Note that , concurrent with our work , refinements to the residual network paradigm further improve the state - of - the - art on ImageNet . Wide residual networks wideresnet of - layers reduce Therefore , we focus on how to learn target - discriminative representations considering the third term . In [ reference ] ) the focus was on the point we have stated and a target - specific classifier was constructed using a residual network structure . Different from their method , we constructed a target - specific network by providing artificially labeled target samples . To optimize quality , the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing . One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet , a 22 layers deep network , the quality of which is assessed in the context of classification and detection . An off-the-shelf classification scheme utilizing a linear support vector machine is applied to the high-level convolution features that come before fully connected layers in popular deep learning architectures . Extensive experimental evaluation compared OverFeat , GoogLeNet , VGG , and AlexNet architectures for feature extraction . Results showed that OverFeat features outperform the other architectures with a mAP = 0.7021 on the nine class fine-grained problem which was almost 0.02 better than its closest competitor , GoogLeNet , which performed best on smaller vessel types . This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks ( CNNs ) . The main contributions of this paper are ( 1 ) a manually collected and validated crop pest dataset is described and shared ; ( 2 ) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes , with pest classification results better than the original model ; and ( 3 ) the fine-tuned GoogLeNet model obtains an improvement of 6.22 % compared to the state-of-the-art method . As a result , the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification . In this paper , we propose a novel Joint Bayesian guided metric learning technique for dealing with the face verification task , which well integrates the above two steps of face verification into an end-to-end convolutional neural network ( CNN ) architecture . In the training stage , an initial neural network , which has the similar architecture with GoogLeNet CNN model , is firstly pre-trained by optimizing classification-based objective functions on the publicly available CASIA WebFace database . Based on constructed face pairs dataset from CASIA WebFace and LFW datasets , we then fine-tune the whole network parameters under the guide of the learned knowledge , which is obtained from the highly successful Joint Bayesian model . OBJECTIVES The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts , dentigerous cysts , and periapical cysts-using dental panoramic radiography and cone beam computed tomographic ( CBCT ) images based on a deep convolutional neural network ( CNN ) . METHODS The GoogLeNet Inception-v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning . Diagnostic indices ( area under the ROC curve [ AUC ] , sensitivity , specificity , and confusion matrix with and without normalization ) were calculated and compared between pretrained models using panoramic and CBCT images . To overcome the challenge of minimal labeled training data and domain shift from the training images taken under the controlled lab environment to the consumer images taken under natural living conditions , several data augmentation techniques are applied on the Region of Interest to generate synthetic pill images for training the CNN . We adopted GoogLeNet Inception Network as our main classifier . Three GoogLeNet models with different specialties on color , shape and feature are trained on the augmented dataset . We employ the faster R-CNN and deep residual network for performing object detection . Further , we adopt the GoogLeNet Inception V3 network to enhance the discrimination of the appearance features , which explicitly leverages the appearance cues to improve the trajectory associations . Because frequent deformations and occlusions are the main problems that lead to the inconsistent appearances of pedestrians , we use spatial and temporal feature representations with discriminative mixed features to overcome these problems . Our main goal is to better understand the generalization abilities of these networks and their learned inner representations . We show that both GoogLeNet and AlexNet networks are largely unable to recognize abstract sketches that are easily recognizable by humans . Moreover , we show that the measured efficacy vary considerably across different classes and we discuss possible reasons for this . We variate batch sizes between trainings and estimate the average training time per iteration and per image on each configuration . Both frameworks presented similar times for the AlexNet model , and TensorFlow outperforms Caffe by presenting times up to 2 times lower than Caffe for the GoogLeNet Model . The work also presents the impact of lack of support by the frameworks for NUMA Architectures , and relates a problem stated on loss computation by the Caffe Framework . For this , we colorize the images using an existing Lab colorization model , both with and without class rebalancing , and our novel HSV colorization model without class rebalancing . Then we construct gender classification models on the grayscale and colorized datasets using a reduced GoogLeNet convolutional neural network . Several models are trained using different loss functions ( cross entropy loss , hinge loss ) and gradient optimization solvers ( Nesterov 's Accelerated Gradient Descent , Stochastic Gradient Descent ) , initialized using both random and pre-trained weights . In the field of agricultural information , the automatic identification and diagnosis of maize leaf diseases is highly desired . To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters , the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper . Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters , changing the pooling combinations , adding dropout operations and rectified linear unit functions , and reducing the number of classifiers . section : CNN Architectures We experiment with VGGNet [ reference ] , GoogLeNet [ reference ] , ResNets [ reference ] , and DenseNets [ reference ] as base architectures for the Siamese network trained with PC to demonstrate that our method is insensitive to the choice of source architecture . section : Experimental Details 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 . For the interpretation of this table , please consult . The notable architecture changes compared to the GoogLeNet model include : The 5 5 convolutional layers are replaced by two consecutive 3 3 convolutional layers . This increases the maximum depth of the network by 9 weight layers . No new data sources were used , for example , by the top entries in the ILSVRC 2014 competition besides the classification dataset of the same competition for detection purposes . Our GoogLeNet submission to ILSVRC 2014 actually uses fewer parameters than the winning architecture of Krizhevsky et al from two years ago , while being significantly more accurate . The biggest gains in object - detection have not come from the utilization of deep networks alone or bigger models , but from the synergy of deep architectures and classical computer vision , like the R - CNN algorithm by Girshick et al . However , contrary to the fixed 2 - layer deep model of , all filters in the Inception model are learned . Furthermore , Inception layers are repeated many times , leading to a 22 - layer deep model in the case of the GoogLeNet model . Network - in - Network is an approach proposed by Lin et al . Our networks were trained using the DistBelief distributed machine learning system using modest amount of model and data - parallelism . Although we used CPU based implementation only , a rough estimate suggests that the GoogLeNet network could be trained to convergence using few high - end GPUs within a week , the main limitation being the memory usage . Our training used asynchronous stochastic gradient descent with 0.9 momentum , fixed learning rate schedule ( decreasing the learning rate by 4 % every 8 epochs ) . The initial convolutional layers of the network extract features from the image while the fully connected layers predict the output probabilities and coordinates . Our network architecture is inspired by the GoogLeNet model for image classification . Our network has 24 convolutional layers followed by 2 fully connected layers . section : Factorizing Convolutions with Large Filter Size Much of the original gains of the GoogLeNet network arise from a very generous use of dimension reduction . This can be viewed as a special case of factorizing convolutions in a computationally efficient manner . Compared to previous methods , our NAN achieves a mean accuracy of 95.72 % , reducing the error of FaceNet by 12.3 % . Note that FaceNet is also based on a GoogLeNet style network , and the average similarity of all pairs of 100 frames in each video ( i.e. , 10 K pairs ) was used [ reference ] . To our knowledge , only the VGG - Face [ reference ] achieves an accuracy ( 97.3 % ) higher than ours . However , that result is based on a further discriminative metric learning on YTF , without which the accuracy is only 91.5 % [ reference ] . section : Introduction ( a ) Dilated convolution based inception module ( b ) Original GoogLeNet inception module In this paper , we focus on single image super - resolution ( SR ) , which aims at recovering a high - resolution ( HR ) image from a given low - resolution ( LR ) one . Currently , the two best nets on ImageNet dataset are GoogLeNet ( Table [ reference ] ) and VGG 19 - layer network . Using multi - scale multi - crop testing , a single GoogLeNet net achieves top - 5 error . With multi - scale dense evaluation , a single VGG 19 - layer net obtains top - 1 and top - 5 errors and and improves top - 5 error of GoogLeNet by . We tested a variant LSUV initialization which was normalizing input activations of the each layer instead of output ones . Normalizing the input or output is identical for standard feed - forward nets , but normalizing input is much more complicated for networks with maxout ( ) or for networks like GoogLeNet ( ) which use the output of multiple layers as input . Input normalization brought no improvement of results when tested against the LSUV Algorithm [ reference ] , LSUV was also tested with pre - initialization of weights with Gaussian noise instead of orthonormal matrices . It has a total of 140 million parameters and requires around 1.6 billion FLOPS per image . The second category we use is based on GoogLeNet style Inception models . These models have fewer parameters ( around 6.6M - 7.5 M ) and up to fewer FLOPS ( between 500M - 1.6B ) . Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures . In addition , we describe several architectural innovations , including Fractal of FractalNet network , Stagewise Boosting Networks , and Taylor Series Networks ( our Caffe code and prototxt files are available at https://github.com/iPhysicist/CNNDesignPatterns ) . We hope others are inspired to build on our preliminary work .
Reading the text embedded in natural scene images is essential to many applications . In this paper , we propose a method for detecting text in scene images based on multi-level connected component ( CC ) analysis and learning text component features via convolutional neural networks ( CNN ) , followed by a graph-based grouping of overlapping text boxes . The multi-level CC analysis allows the extraction of redundant text and non-text components at multiple binarization levels to minimize the loss of any potential text candidates .
Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases , without considering the topological structure information among these nodes , which is important for predicting lncRNA – disease associations . We propose a method based on information flow propagation and convolutional neural networks , called LDAPred , to predict disease-related lncRNAs . LDAPred not only integrates the similarities , associations , and interactions among lncRNAs , diseases , and miRNAs , but also exploits the topological structures formed by them .
We propose a powerful adaptation of the state-of-the-art Speech Recognition models for these tasks and demonstrate the effectiveness of our techniques on standard datasets . Our architectures are a combination of Bidirectional Long Short Term Memory ( LSTM ) Networks , Convolutional Networks , and Fully Connected Networks , trained by Gradient Descent to minimize the Cross Entropy and the Connectionist Temporal Classification ( CTC ) losses . We adapt the Libri Speech corpus for the task of segmentation and diarization .
In this paper , we study such features obtained from a compact short-range 60-GHz frequency modulated continuous wave radar for various human activities . We present novel Convolutional Neural Network architectures to leverage these features for human activity classification .
Tree - structured models treat each word token as a node in a syntactic parse tree and learn sentence representations from leaves to the root in a recursive manner . Convolutional neural networks ( CNNs ) and recurrent neural networks ( RNNs ) have emerged as two widely used architectures and are often combined with sequence - based or tree - structured models . Owing to the capability of capturing local correlations of spatial or temporal structures , CNNs have achieved top performance in computer vision , speech recognition and NLP .
For our experiments we resized all images to pixels . appendix : Convolutional Neural Networks Our models for CIFAR - 10 and ImageNet are deep , feed - forward convolutional neural networks ( CNNs ) .
CNN : NIPS and CNN : NAACL apply convolutional neural networks ( CNNs ) as the individual sentence model , where a set of feature detectors over successive words are designed to extract local features . LSTM :
Compared to the 2013 result , the accuracy has almost doubled . The top performing teams all use Convolutional Networks . We report the official scores in Table [ reference ] and common strategies for each team : the use of external data , ensemble models or contextual models .
However , there has been no systematic research on the predictive power and configurations of different deep neural networks in this task . In this work , we systematically explore the configurations and performances of three adapted deep neural networks : ResNet , LSTM , and hybrid ResNet-LSTM . Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset .
The proposed network exploits the rich information extracted from the transform domain to reconstruct more accurate high-resolution images . In addition , to reap the benefits from both residual network ( ResNet ) and densely convolutional network ( DenseNet ) topologies , we use dual-path blocks as the basic building blocks which allow feature re-use while ensuring the ability to continue extracting new features . Thanks to extensive research on the attention mechanism , we further introduce spatial and self-attention blocks to refine features based on feature correlations at different layers .
In this article , we collected and labeled a dataset containing 9 types of ground weather images . In addition , we use ResNet convolutional neural network and DenseNet convolutional neural network to build the network structure , and perform probability discrimination on the output results of each model to increase the recognition rate . We trained and tested on the collected weather dataset .
Based on the shape features of pieces , an anchor mechanism is designed to detect pieces . The method of extracting eight-direction features and rotating invariant HOG features are used to train the ResNet convolutional neural network , which can improve the problem of fewer types of training-intensive chess pieces , single rotating pictures , etc . Experimental results show that the proposed convolutional neural network for chess detection and recognition can greatly enhance the detection of environmental adaptability , robustness , and improve the detection speed and the recognition accuracy .
Thus , this study proposes a HS reconstruction , which fuses merely two RGB images with redundant spectral responses . In this work , we design a new RGB camera via shifting the SS of an existed RGB camera , which can provide similar strength of spectral response with different spectral centers of SS , and fuse the new achieved color image with an existed RGB image by a deep ResNet . Experiments validate that fusion of two existed RGB images can provide impressive HS reconstruction performance and further improvement can be achieved by integrating the color image of the simulated SS with the RGB image .
In terms of the datasets , a high sample-identity ratio is conducive to generalization , but it leads to increased difficulty for the training to converge . For the network architecture , deep ResNet has an advantage over other designs . Various normalization operations in the network are also necessary .
Methods CEUS images of 527 patients over three phases ( un-enhanced , early arterial and late portal phases ) were manually pre-processed into square frames . Supervised CNN was designed as the 152-layer ResNet trained on each phase frames separately and assembled by SVM algorithm . The model was trained on 55350 augmented frames and tested on 474 un-augmented frames .
We evaluate the quality and diversity of generated samples with the robust metric Fréchet Inception Distance , a measure that can detect the intra-class mode dropping and is consistent with human assessment . In addition , we conduct a battery of experiments with the widely used architecture DCGAN and the complex architecture ResNet to demonstrate the effectiveness and robustness of the proposed method .
The method introduced in this paper aims at helping computer vision practitioners faced with an overfit problem . The idea is to replace , in a 3-branch ResNet , the standard summation of residual branches by a stochastic affine combination . The largest tested model improves on the best single shot published result on CIFAR10 by reaching 2.86 % test error .
Second , for computational neuroscience , CNNs provide a testable modelling platform for visual categorizations inspired by the multi-layered organization of visual cortex [ 7 ] . Here , we used a 3D generative model to control the variance of information learned to identify 2,000 face identities in one CNN architecture ( 10-layer ResNet [ 9 ] ) . We generated 25 M face images to train the network by randomly sampling intrinsic ( i.e. face morphology , gender , age , expression and ethnicity ) and extrinsic factors of face variance ( i.e. 3D pose , illumination , scale and 2D translation ) .
To address this , we propose an end-to-end deep blind synthesized image quality metric SIQA-CFP . We particularly design a contextual multilevel feature pooling module to encode low- and high-level features , which are extracted by a deep pre-trained ResNet . Experimental results on IRCCyN/IVC DIBR dataset show that our method outperforms state-of-the-art synthesized image quality metrics .
A novel cascade learning framework to incrementally train deeper and more accurate convolutional neural networks is introduced . The proposed cascade learning facilitates the training of deep efficient networks with plain convolutional neural network ( CNN ) architectures , as well as with residual network ( ResNet ) architectures . This is demonstrated on the problem of image super-resolution ( SR ) .
Our newly collected Naver Maps ' Street View dataset is used for training regression ResNet-34 and for comparison with previous methods . It is concluded that the trained regression ResNet outperforms previous methods in terms of both computation time and accuracy .
U - Nets are designed based on the residual modules . More recently , the DenseNet outperforms the ResNet in the image classification task , benefitting from its dense connections . We would like to use the dense connectivity into multiple U - Nets .
The central idea is that using deeper networks should not only lead to improvements in classification but also object detection . Some recent work in this direction include ResNet , Inception - ResNet and ResNetXt for object detection . The second area of research has been to use contextual reasoning , proxy tasks for reasoning and other top - down mechanisms for improving representations for object detection .
Existing approaches , such as the DeepDocClassifier , apply standard Convolutional Network architectures with transfer learning from the object recognition domain . The contribution of the paper is threefold : First , it investigates recently introduced very deep neural network architectures ( GoogLeNet , VGG , ResNet ) using transfer learning ( from real images ) . Second , it proposes transfer learning from a huge set of document images , i.e. documents .
Tab . [ reference ] summarizes impact of different schemes on iSQRT - COV network with ResNet - 50 architecture . Without post - compensation , iSQRT - COV network fails to converge .
We resize all images to resolution 256 × 512 for computational efficiency . As a shared representation function ( encoder ) , we use the ResNet - 50 architecture [ reference ] in fully - convolutional fashion . We take the ResNet - 50 architecture and only use layers prior to average pooling that are fully convolutional .
In the testing phase , we abandon this auxiliary branch and only use the well optimized master branch for final prediction . This kind of deeply supervised training strategy for ResNet - based FCN is broadly useful under different experimental settings and works with the pre - trained ResNet model . This manifests the generality of such a learning strategy .
The global pyramid pooling feature provides additional contextual information . We have also provided a deeply supervised optimization strategy for ResNet - based FCN network . We hope the implementation details publicly available can help the community adopt these useful strategies for scene parsing and semantic segmentation and advance related techniques .
Residual networks he2015deep , or ResNets , lead a recent and dramatic increase in both depth and accuracy of convolutional neural networks , facilitated by constraining the network to learn residuals . ResNet variants he2015deep , he2016identity , huang2016stochasticdepth and related architectures
In the following we describe our findings w.r.t . the different ResNet block architectures and also analyze the performance of our proposed wide residual networks . We note that for all experiments related to ¡ ¡ type of convolutions in a block ¿ ¿ and ¡ ¡ number of convolutions per block
subsection : Convolutional Neural Network The ResNet - 50 model is used in our experiment . We resize the generated images to using bilinear sampling .
To evaluate the performance of CORAL for age estimation from face images , we chose the ResNet - 34 architecture , which is a modern CNN architecture that is known for achieving good performance on a variety of image classification tasks . For the remainder of this paper , we refer to the original ResNet - 34 CNN with cross entropy loss as CE - CNN . To implement CORAL , we replaced the last output layer with the corresponding binary tasks ( Figure [ reference ] ) and refer to this CNN as CORAL - CNN .
The pre - activation version reaches slightly higher training loss at convergence , but produces lower test error . This phenomenon is observed on ResNet - 110 , ResNet - 110 ( 1 - layer ) , and ResNet - 164 on both CIFAR - 10 and 100 . This is presumably caused by BN ’s regularization effect .
Firstly , we decompose the original signal into low frequency components and high frequency components using wavelet packet analysis and we construct a energy model of the cell voltage signal for the calculation of the proportion of high frequency fault energy consumption of the original voltage signal . Then we build a dataset of low frequency component samples of cell voltage signal and we train a residual network model on this dataset for the identification of the fluctuations of low-frequency component of cell voltage . Finally , some expert rules are developed to determine the state of cell voltage .
Abstract A non-intrusive sequence to sequence load disaggregation method based on deep delated convolution residual network is proposed in this work . Firstly , the original power data is normalized , and then the sliding window used to create the input for the residual network . The dilated convolution residual network model reduces the difficulty of network optimization and solves the problem of vanishing gradient .
Based on conventional beamforming maps , the proposed method aims at estimating the source coordinates and the strength . The residual network architecture , a well-established model in the field of image recognition , is successfully applied to this task . The investigation reveals a method that fast and accurately renders the position and strength of an unknown source .
The proposed method uses an adaptive 1D separable convolution with residual connection network to classify gear pitting faults with mixed operating conditions . Compared to the traditional convolutional neural network , the separable convolution with residual connection network can carry out the channel convolution with point-by-point convolution to effectively reduce the number of network parameters . The residual connection can solve the representational bottleneck problem of the features in the model .
ResultsOur model achieves F1-score of 77.80 % on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches . ConclusionThe residual network-based model can reduce the negative impact of corpus noise to parameter learning , and the combination of character position attention mechanism will enhance the identification features of different type of entities .
section : Addendum : Shortcut vs. Residual In later experiments , we found that a residual connection can achieve similar accuracies with fewer number of parameters , compared to a shortcut connection . Therefore , in order to reduce the model size and to also follow the SNLI leader - board settings ( e.g. , 300D and 600D embeddings ) , we performed some additional SNLI experiments with the shortcut connections replaced with residual connections , where the input to each next biLSTM layer is the concatenation of the word embedding and the summation of outputs of all previous layers ( related to ResNet in computer vision ) .
The first is a multi - head self - attention mechanism , and the second is a simple , position - wise fully connected feed - forward network . We employ a residual connection he2016deep around each of the two sub - layers , followed by layer normalization .
The decoder contains two LSTM layers . Residual connection He2016DeepRL with a scaling factor of is applied to the two decoder states to compute the outputs .
subsection : Training on JFT - 300 M Data Although there are several novel ConvNet architectures recently proposed , we decide to use a standard Residual Network architecture with 101 layers ( ResNet - 101 ) for its state - of - the - art performance and the ease of comparison with previous work . To train a ResNet - 101 model on JFT - 300 M , We add a fully - connected layer with 18291 outputs at the end of the network for classification .
section : B. Baseline Evaluation In Section 5.2 of the main paper , we describe the setting of our baseline method ( Residual Network Baseline ) and compare it to our FRRN A network . To emphasize on a proper training procedure of both baselines , Figure 9 shows the mean IoU score on the validation set over time .
Table [ reference ] shows that FractalNet scales to ImageNet , matching ResNet he2015deep at equal depth . Note that , concurrent with our work , refinements to the residual network paradigm further improve the state - of - the - art on ImageNet . Wide residual networks wideresnet of - layers reduce
Therefore , we focus on how to learn target - discriminative representations considering the third term . In [ reference ] ) the focus was on the point we have stated and a target - specific classifier was constructed using a residual network structure . Different from their method , we constructed a target - specific network by providing artificially labeled target samples .
To optimize quality , the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing . One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet , a 22 layers deep network , the quality of which is assessed in the context of classification and detection .
An off-the-shelf classification scheme utilizing a linear support vector machine is applied to the high-level convolution features that come before fully connected layers in popular deep learning architectures . Extensive experimental evaluation compared OverFeat , GoogLeNet , VGG , and AlexNet architectures for feature extraction . Results showed that OverFeat features outperform the other architectures with a mAP = 0.7021 on the nine class fine-grained problem which was almost 0.02 better than its closest competitor , GoogLeNet , which performed best on smaller vessel types .
This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks ( CNNs ) . The main contributions of this paper are ( 1 ) a manually collected and validated crop pest dataset is described and shared ; ( 2 ) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes , with pest classification results better than the original model ; and ( 3 ) the fine-tuned GoogLeNet model obtains an improvement of 6.22 % compared to the state-of-the-art method . As a result , the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification .
In this paper , we propose a novel Joint Bayesian guided metric learning technique for dealing with the face verification task , which well integrates the above two steps of face verification into an end-to-end convolutional neural network ( CNN ) architecture . In the training stage , an initial neural network , which has the similar architecture with GoogLeNet CNN model , is firstly pre-trained by optimizing classification-based objective functions on the publicly available CASIA WebFace database . Based on constructed face pairs dataset from CASIA WebFace and LFW datasets , we then fine-tune the whole network parameters under the guide of the learned knowledge , which is obtained from the highly successful Joint Bayesian model .
OBJECTIVES The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)-odontogenic keratocysts , dentigerous cysts , and periapical cysts-using dental panoramic radiography and cone beam computed tomographic ( CBCT ) images based on a deep convolutional neural network ( CNN ) . METHODS The GoogLeNet Inception-v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning . Diagnostic indices ( area under the ROC curve [ AUC ] , sensitivity , specificity , and confusion matrix with and without normalization ) were calculated and compared between pretrained models using panoramic and CBCT images .
To overcome the challenge of minimal labeled training data and domain shift from the training images taken under the controlled lab environment to the consumer images taken under natural living conditions , several data augmentation techniques are applied on the Region of Interest to generate synthetic pill images for training the CNN . We adopted GoogLeNet Inception Network as our main classifier . Three GoogLeNet models with different specialties on color , shape and feature are trained on the augmented dataset .
We employ the faster R-CNN and deep residual network for performing object detection . Further , we adopt the GoogLeNet Inception V3 network to enhance the discrimination of the appearance features , which explicitly leverages the appearance cues to improve the trajectory associations . Because frequent deformations and occlusions are the main problems that lead to the inconsistent appearances of pedestrians , we use spatial and temporal feature representations with discriminative mixed features to overcome these problems .
Our main goal is to better understand the generalization abilities of these networks and their learned inner representations . We show that both GoogLeNet and AlexNet networks are largely unable to recognize abstract sketches that are easily recognizable by humans . Moreover , we show that the measured efficacy vary considerably across different classes and we discuss possible reasons for this .
We variate batch sizes between trainings and estimate the average training time per iteration and per image on each configuration . Both frameworks presented similar times for the AlexNet model , and TensorFlow outperforms Caffe by presenting times up to 2 times lower than Caffe for the GoogLeNet Model . The work also presents the impact of lack of support by the frameworks for NUMA Architectures , and relates a problem stated on loss computation by the Caffe Framework .
For this , we colorize the images using an existing Lab colorization model , both with and without class rebalancing , and our novel HSV colorization model without class rebalancing . Then we construct gender classification models on the grayscale and colorized datasets using a reduced GoogLeNet convolutional neural network . Several models are trained using different loss functions ( cross entropy loss , hinge loss ) and gradient optimization solvers ( Nesterov 's Accelerated Gradient Descent , Stochastic Gradient Descent ) , initialized using both random and pre-trained weights .
In the field of agricultural information , the automatic identification and diagnosis of maize leaf diseases is highly desired . To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters , the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper . Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters , changing the pooling combinations , adding dropout operations and rectified linear unit functions , and reducing the number of classifiers .
section : CNN Architectures We experiment with VGGNet [ reference ] , GoogLeNet [ reference ] , ResNets [ reference ] , and DenseNets [ reference ] as base architectures for the Siamese network trained with PC to demonstrate that our method is insensitive to the choice of source architecture . section : Experimental Details
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 .
For the interpretation of this table , please consult . The notable architecture changes compared to the GoogLeNet model include : The 5 5 convolutional layers are replaced by two consecutive 3 3 convolutional layers . This increases the maximum depth of the network by 9 weight layers .
No new data sources were used , for example , by the top entries in the ILSVRC 2014 competition besides the classification dataset of the same competition for detection purposes . Our GoogLeNet submission to ILSVRC 2014 actually uses fewer parameters than the winning architecture of Krizhevsky et al from two years ago , while being significantly more accurate . The biggest gains in object - detection have not come from the utilization of deep networks alone or bigger models , but from the synergy of deep architectures and classical computer vision , like the R - CNN algorithm by Girshick et al .
However , contrary to the fixed 2 - layer deep model of , all filters in the Inception model are learned . Furthermore , Inception layers are repeated many times , leading to a 22 - layer deep model in the case of the GoogLeNet model . Network - in - Network is an approach proposed by Lin et al .
Our networks were trained using the DistBelief distributed machine learning system using modest amount of model and data - parallelism . Although we used CPU based implementation only , a rough estimate suggests that the GoogLeNet network could be trained to convergence using few high - end GPUs within a week , the main limitation being the memory usage . Our training used asynchronous stochastic gradient descent with 0.9 momentum , fixed learning rate schedule ( decreasing the learning rate by 4 % every 8 epochs ) .
The initial convolutional layers of the network extract features from the image while the fully connected layers predict the output probabilities and coordinates . Our network architecture is inspired by the GoogLeNet model for image classification . Our network has 24 convolutional layers followed by 2 fully connected layers .
section : Factorizing Convolutions with Large Filter Size Much of the original gains of the GoogLeNet network arise from a very generous use of dimension reduction . This can be viewed as a special case of factorizing convolutions in a computationally efficient manner .
Compared to previous methods , our NAN achieves a mean accuracy of 95.72 % , reducing the error of FaceNet by 12.3 % . Note that FaceNet is also based on a GoogLeNet style network , and the average similarity of all pairs of 100 frames in each video ( i.e. , 10 K pairs ) was used [ reference ] . To our knowledge , only the VGG - Face [ reference ] achieves an accuracy ( 97.3 % ) higher than ours . However , that result is based on a further discriminative metric learning on YTF , without which the accuracy is only 91.5 % [ reference ] .
section : Introduction ( a ) Dilated convolution based inception module ( b ) Original GoogLeNet inception module In this paper , we focus on single image super - resolution ( SR ) , which aims at recovering a high - resolution ( HR ) image from a given low - resolution ( LR ) one .
Currently , the two best nets on ImageNet dataset are GoogLeNet ( Table [ reference ] ) and VGG 19 - layer network . Using multi - scale multi - crop testing , a single GoogLeNet net achieves top - 5 error . With multi - scale dense evaluation , a single VGG 19 - layer net obtains top - 1 and top - 5 errors and and improves top - 5 error of GoogLeNet by .
We tested a variant LSUV initialization which was normalizing input activations of the each layer instead of output ones . Normalizing the input or output is identical for standard feed - forward nets , but normalizing input is much more complicated for networks with maxout ( ) or for networks like GoogLeNet ( ) which use the output of multiple layers as input . Input normalization brought no improvement of results when tested against the LSUV Algorithm [ reference ] , LSUV was also tested with pre - initialization of weights with Gaussian noise instead of orthonormal matrices .
It has a total of 140 million parameters and requires around 1.6 billion FLOPS per image . The second category we use is based on GoogLeNet style Inception models . These models have fewer parameters ( around 6.6M - 7.5 M ) and up to fewer FLOPS ( between 500M - 1.6B ) .
Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures . In addition , we describe several architectural innovations , including Fractal of FractalNet network , Stagewise Boosting Networks , and Taylor Series Networks ( our Caffe code and prototxt files are available at https://github.com/iPhysicist/CNNDesignPatterns ) . We hope others are inspired to build on our preliminary work .
Learning Text Component Features via Convolutional Neural Networks for Scene Text Detection LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs Says Who? Deep Learning Models for Joint Speech Recognition, Segmentation and Diarization Human Activity Classification with Frequency Modulated Continuous Wave Radar Using Deep Convolutional Neural Networks A C-LSTM Neural Network for Text Classification Improving neural networks by preventing co-adaptation of feature detectors Natural Language Inference by Tree-Based Convolution and Heuristic Matching Going deeper with convolutions A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition Modified Dual Path Network With Transform Domain Data for Image Super-Resolution Research on Multi-class Weather Classification Algorithm Based on Multi-model Fusion Research on Chinese Chess Detection and Recognition Based on Convolutional Neural Network Hyperspectral Reconstruction with Redundant Camera Spectral Sensitivity Functions Systematic evaluation of deep face recognition methods IDDF2019-ABS-0148 Focal liver lesion classification using a convolutional neural network based transfer-learning algorithm on tri-phase images of contrast-enhanced ultrasound Generative Adversarial Networks Based on Denoising and Reconstruction Regularization Shake-Shake regularization of 3-branch residual networks Using Psychophysical Methods to Understand Mechanisms of Face Identification in a Deep Neural Network Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling CNF+CT: Context Network Fusion of Cascade-Trained Convolutional Neural Networks for Image Super-Resolution Regression with residual neural network for vanishing point detection Quantized Densely Connected U-Nets for Efficient Landmark Localization A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization Multi-Task Learning as Multi-Objective Optimization Pyramid Scene Parsing Network FractalNet: Ultra-Deep Neural Networks without Residuals Wide Residual Networks Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Identity Mappings in Deep Residual Networks A Cell Voltage Classification Method Based on Wavelet Packet Analysis and Residual Network Non-intrusive load disaggregation based on deep dilated residual network A deep learning method for grid-free localization and quantification of sound sources. Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection Attention-based deep residual learning network for entity relation extraction in Chinese EMRs Shortcut-Stacked Sentence Encoders for Multi-Domain Inference Attention is All you Need Compressing Word Embeddings via Deep Compositional Code Learning Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes FractalNet: Ultra-Deep Neural Networks without Residuals Asymmetric Tri-training for Unsupervised Domain Adaptation Going deeper with convolutions Off-the-Shelf CNN Features for Fine-Grained Classification of Vessels in a Maritime Environment Crop pest recognition in natural scenes using convolutional neural networks Joint Bayesian guided metric learning for end-to-end face verification Diagnosis of cystic lesions using panoramic and CBCT images based on deep learning neural network. Pill Recognition Using Minimal Labeled Data Anomaly detection by exploiting the tracking trajectory in surveillance videos On the Performance of GoogLeNet and AlexNet Applied to Sketches Performance Evaluation of Deep Learning Frameworks over Different Architectures Deep Colorization for Facial Gender Recognition Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks Pairwise Confusion for Fine-Grained Visual Classification FractalNet: Ultra-Deep Neural Networks without Residuals Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Going deeper with convolutions You Only Look Once: Unified, Real-Time Object Detection Rethinking the Inception Architecture for Computer Vision Neural Aggregation Network for Video Face Recognition Single image super-resolution with dilated convolution based multi-scale information learning inception module HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition All you need is a good init FaceNet: A unified embedding for face recognition and clustering Deep Convolutional Neural Network Design Patterns
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