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Deep Gaussian Processes with Convolutional Kernels
[article]
2018
arXiv
pre-print
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Our model learns local spatial influence and outperforms strong GP based baselines on multi-class image classification. ...
In this paper, we build on the recent convolutional GP to develop Convolutional DGP (CDGP) models which effectively capture image level features through the use of convolution kernels, therefore opening ...
Deep Gaussian processes with convolutional kernels Convolutional DGP considers multiple functions from a GP prior with convolutional kernels to form a representation of the image in the first layer. ...
arXiv:1806.01655v1
fatcat:qmzblivavbfbvkz5u25bhjldo4
Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image
2019
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
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The process of selection this salak fruits used convolutional neural network (CNN) based on image of salak fruits. ...
This research was conducted to increase accuracy value the classification of salak exported based on previous research. ...
(t)= result function of convolution operation x = multi-dimensional of array data w = weight or kernel t = variable from function a = dummy variable
Figure 2 2 Pooling with max-pooling
195 Figure 3 ...
doi:10.22146/ijccs.42036
fatcat:7ulgl3pqezaqnfh6dpl5nf42du
Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model
2021
Journal of Applied Mathematics and Physics
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Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. ...
image. ...
Acknowledgements In the process of completing the study, the author thanks for being supported by Y. Sun, L. W. Chen DOI: 10.4236/jamp.2021.912204 ...
doi:10.4236/jamp.2021.912204
fatcat:mtnms3a6c5dyvinvbyihfwzebi
Bayesian Image Classification with Deep Convolutional Gaussian Processes
[article]
2020
arXiv
pre-print
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We also reformulate GP image-to-image convolutional mappings as multi-output GPs, leading to deep convolutional GPs. ...
This has limited their applicability in certain tasks (e.g. image classification). ...
We would like to thank Fergus Simpson, Hugh Salimbeni, ST John, Victor Picheny, and anonymous reviewers for helpful feedback on the manuscript. ...
arXiv:1902.05888v2
fatcat:lkeew5cndvhthiqbe5o6c2zox4
Learning Filter Scale and Orientation In CNNs
[article]
2018
arXiv
pre-print
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We tested the new filter model on MNIST, MNIST-cluttered, and CIFAR-10 and compared the results with the networks that used conventional convolution layers. ...
The proposed model uses a relatively large base size (grid) for filters. In the grid, a differentiable function acts as an envelope for the filters. ...
Discussion In this paper, we propose an adaptive convolution filter model based on a Gaussian kernel that is acting as an envelope function on shared filter weights. ...
arXiv:1803.00388v1
fatcat:ki3cw26j6jaxzoi52uhuoyce6y
Evaluation of Deep Gaussian Processes for Text Classification
2020
International Conference on Language Resources and Evaluation
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Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. ...
With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models ...
Section 3 presents a background on Gaussian Process (GP) and Deep Gaussian Process (DGP) models. Section 4 elaborates on the Convolutional Deep Gaussian Process (CDGP) model for Text Classification. ...
dblp:conf/lrec/JayashreeS20
fatcat:z55mltfkurhgplh4lvc3iytooe
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network
2017
Remote Sensing
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multi-resolution image classification. ...
We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. ...
a Gaussian kernel function that takes feature as input (denoted by and extracted for pixel and ). ...
doi:10.3390/rs9050498
fatcat:ng5e5qhghngf5ac2hzcc5bpgvi
Anti-inference ability of CNN mainstream models based on face mask detection
2023
Applied and Computational Engineering
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Picture recognition is mature technology with the development of convolution neural network (CNN). ...
Camera with this function can be easily established for face mask detection, providing unmanned detection for safety concern. Picture noise occurs for long-term using of detection equipment. ...
Before training of CNN model, image resizing is implemented based on image processing function of OpenCV. Based on torchvision.model [16], all images are reshaped to size of 224×224. ...
doi:10.54254/2755-2721/2/20220575
fatcat:wr3cug4scva2hm4jqm3xnyluga
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
[article]
2019
arXiv
pre-print
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In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural ...
network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model. ...
In this paper, the deep neural network for lung nodule classification is built based on multi-level convolutional neural networks, which designs three levels of CNNs with same structure but different convolutional ...
arXiv:1901.00276v1
fatcat:xrfnj5mqsjaipjfw3deywxofue
Gaussian Filter in CRF Based Semantic Segmentation
[article]
2017
arXiv
pre-print
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In this paper, we introduce a multi-resolution neural network for FCN and apply Gaussian filter to the extended CRF kernel neighborhood and the label image to reduce the oscillating effect of CRF neural ...
Fully convolutional network [1] is the standard model for semantic segmentation. ...
It establishes and optimizes multi-layered or self-adjusted functions for detection, classification, segmentation etc.. ...
arXiv:1709.00516v1
fatcat:yjfivat6yjbelknyx5neb3e56a
Convolutional Neural Network Based Facial Expression Recognition Using Image Filtering Techniques
2021
International Journal of Intelligent Engineering and Systems
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To improve its performance the input facial images displaying emotions are subject to two kinds of filtering: One by Gaussian filter and another by LoG. ...
The classification of seven emotions by the CNN model on the filtered facial images from Extended Cohn-Kanade (CK+) dataset comprising 981 images has led to the attainment of 100% recognition accuracy ...
These patterns [9] and image based processes like coupled Gaussian [11] can be used for multi-view FER for the development of pose-invariant methods. ...
doi:10.22266/ijies2021.1031.08
fatcat:sdkyjt3bajab7b5bn2invszvpy
Human Interaction Recognition Based on Whole-Individual Detection
2020
Sensors
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Regarding the complexity of interactive action features, we propose a multi-feature fusion network algorithm based on parallel Inception and ResNet. ...
and obtains higher classification accuracy. ...
N convolution kernel and an N × 1 convolution kernel. ...
doi:10.3390/s20082346
pmid:32326059
pmcid:PMC7219257
fatcat:l53x72iopnfc5od2jm6wi74gnm
Brain Tumor Detection using Convolutional Neural Network
2021
Turkish Journal of Computer and Mathematics Education
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The fundamentals of MRI are to develop images based on magnetic field and radio waves of the anatomy of the body. The major area of segmentation of images is medical image processing. ...
Recently Convolutional Neural Network plays an important role in medical field and computer vision. One of its application is the identification of brain tumor. ...
Activation function is in between the convolution and pooling layer. ...
doi:10.17762/turcomat.v12i11.5946
fatcat:govr3aosh5brba6ru2vodmahra
Locally Smoothed Neural Networks
[article]
2017
arXiv
pre-print
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Specifically, a multi-variate Gaussian function is utilized to generate the smoother, for modeling the location relations among different local receptive fields. ...
Experiments on some variant of MNIST clearly show our advantages over CNN and locally connected layer. ...
The goal is to predict the 3 digits in the image. The task, thus, becomes a multi-class multi-label classification problem. ...
arXiv:1711.08132v1
fatcat:cfofru4zajfqzg4qvuoinopzou
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model
[article]
2017
arXiv
pre-print
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To overcome this problem, we propose the disordered graph convolutional neural network (DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a preprocessing layer called the disordered ...
The DGCL uses a mixed Gaussian function to realize the mapping between the convolution kernel and the nodes in the neighborhood of the graph. The output of the DGCL is the input of the CNN. ...
[9] proposed an RW kernel function based on computing the RW kernel functions of common steps for two graphs and proved that this function is a positive-definite function. ...
arXiv:1712.03563v1
fatcat:icbm2ynwgfetxdjvoo7q5scpze
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