scholarly journals Encoding Lexico-Semantic Knowledge using Ensembles of Feature Maps from Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
Steven Derby ◽  
Paul Miller ◽  
Barry Devereux
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Knowledge ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks

scholarly journals Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

2019 ◽  
Author(s):  
Chunlei Liu ◽  
Wenrui Ding ◽  
Xin Xia ◽  
Yuan Hu ◽  
Baochang Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Superior Performance ◽  
Feature Maps ◽  
Unified Framework ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Networks ◽  
Computation Efficiency ◽  
Significant Performance ◽  
Binary Network

Binarized  convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose rectified binary convolutional networks (RBCNs), towards optimized BCNNs, by combining full-precision kernels and feature maps to rectify the binarization process in a unified framework. In particular, we use a GAN to train the 1-bit binary network with the guidance of its corresponding full-precision model, which significantly improves the performance of BCNNs. The rectified convolutional layers are generic and flexible, and can be easily incorporated into existing DCNNs such as WideResNets and ResNets. Extensive experiments demonstrate the superior performance of the proposed RBCNs over state-of-the-art BCNNs. In particular, our method shows strong generalization on the object tracking task.

scholarly journals Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images

2021 ◽  
Vol 11 (7) ◽  
pp. 3155
Author(s):  
Guo-Shiang Lin ◽  
Kuan-Ting Lai ◽  
Jian-Ming Syu ◽  
Jen-Yung Lin ◽  
Sin-Kuo Chai
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Cross Validation ◽  
Normal Skin ◽  
Object Localization ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Subjective Testing ◽  
Instance Segmentation

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.

Phonocardiogram Classification Using Deep Convolutional Neural Networks with Majority Vote Strategy

2019 ◽  
Vol 9 (8) ◽  
pp. 1692-1704
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Jue Wang ◽  
Huiqun Wu ◽  
Hui Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Heart Sound ◽  
Majority Vote ◽  
Heart Sounds ◽  
Cardiac Diseases ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Vote Strategy ◽  
Pcg Signals

Most current automated phonocardiogram (PCG) classification methods are relied on PCG segmentation. It is universal to make use of the segmented PCG signals and then extract efficiency features for computer-aided auscultation or heart sound classification. However, the accurate segmentation of the fundamental heart sounds depends greatly on the quality of the heart sound signals. In addition these methods that heavily relied on segmentation algorithm considerably increase the computational burden. To solve above two issues, we have developed a novel approach to classify normal and abnormal cardiac diseases with un-segmented PCG signals. A deep Convolutional Neural Networks (DCNNs) method is proposed for recognizing normal and abnormal cardiac diseases. In the proposed method, one-dimensional heart sound signals are first converted into twodimensional feature maps which have three channels and each of them represents Mel-frequency spectral coefficients (MFSC) features including static, delta and delta–delta. These artificial images are then fed to the proposed DCNNs to train and evaluate normal and abnormal heart sound signals. We combined the method of majority vote strategy to finally obtain the category of PCG signals. Sensitivity (Se), Specificity (Sp) and Mean accuracy (MAcc) are used as the evaluation metrics. Results: Experiments demonstrated that our approach achieved a significant improvement, with the high Se, Sp, and MAcc of 92.73%, 96.90% and 94.81% respectively. The proposed method improves the MAcc by 5.63% compared with the best result in the CinC Challenge 2016. In addition, it has better robustness performance when applying for the long heart sounds. The proposed DCNNs-based method can achieve the best accuracy performance on recognizing normal and abnormal heart sounds without the preprocessing of segmental algorithm. It significantly improves the classification performance compared with the current state-of-art algorithm.

scholarly journals Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 49 ◽  
Author(s):  
Wei Liu ◽  
Dayu Cheng ◽  
Pengcheng Yin ◽  
Mengyuan Yang ◽  
Erzhu Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Receptive Field ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Remotely Sensed ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Imagery ◽  
Remotely Sensed Images

With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at different scales in remotely sensed images and DCNNs’ fixed receptive field cannot match the scale variability of such objects; (2) Manhole cover objects in large-scale remotely-sensed images are relatively small in size and densely packed, while DCNNs have poor localization performance when applied to such objects. To address these problems, we propose an effective method for detecting manhole cover objects in remotely-sensed images. First, we redesign the feature extractor by adopting the visual geometry group (VGG), which can increase the variety of receptive field size. Then, detection is performed using two sub-networks: a multi-scale output network (MON) for manhole cover object-like edge generation from several intermediate layers whose receptive fields match different object scales and a multi-level convolution matching network (M-CMN) for object detection based on fused feature maps, which combines several feature maps that enable small and densely packed manhole cover objects to produce a stronger response. The results show that our method is more accurate than existing methods at detecting manhole covers in remotely-sensed images.

scholarly journals Semantic relatedness emerges in deep convolutional neural networks designed for object recognition

2020 ◽  
Author(s):  
Taicheng Huang ◽  
Zonglei Zhen ◽  
Jia Liu
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Semantic Relatedness ◽  
Semantic Knowledge ◽  
Human Learning ◽  
Top Down ◽  
Deep Convolutional Neural Networks ◽  
Object Categories ◽  
Semantic Concepts

AbstractHuman not only can effortlessly recognize objects, but also characterize object categories into semantic concepts and construct nested hierarchical structures. Similarly, deep convolutional neural networks (DCNNs) can learn to recognize objects as perfectly as human; yet it is unclear whether they can learn semantic relatedness among objects that is not provided in the learning dataset. This is important because it may shed light on how human acquire semantic knowledge on objects without top-down conceptual guidance. To do this, we explored the relation among object categories, indexed by representational similarity, in two typical DCNNs (AlexNet and VGG11). We found that representations of object categories were organized in a hierarchical fashion, suggesting that the relatedness among objects emerged automatically when learning to recognize them. Critically, the emerged relatedness of objects in the DCNNs was highly similar to the WordNet in human, implying that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects. Finally, the developmental trajectory of the relatedness among objects during training revealed that the hierarchical structure was constructed in a coarse-to-fine fashion, and evolved into maturity before the establishment of object recognition ability. Taken together, our study provides the first empirical evidence that semantic relatedness of objects emerged as a by-product of object recognition, implying that human may acquire semantic knowledge on objects without explicit top-down conceptual guidance.Significance StatementThe origin of semantic concepts is in a long-standing debate, where top-down conceptual guidance is thought necessary to form the hierarchy structure of objects. Here we challenged this hypothesis by examining whether deep convolutional neural networks (DCNNs) for object recognition can emerge the semantic relatedness of objects with no relation information in training object datasets. We found that in the DCNNs representations of objects were organized in a hierarchical fashion, which was highly similar to WordNet in human. This finding suggests that top-down conceptual guidance may not be a prerequisite for human learning the relatedness among objects; rather, semantic relatedness of objects may emerge as a by-product of object recognition.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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