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Toplam kayıt 12, listelenen: 1-10
Analysis of single image super resolution models
(IEEE, 2022-11-18)
Image Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. ...
Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty
(Springer, 2022-04-02)
Although state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being ...
Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images
(Elsevier B.V., 2021-06)
The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis ...
Adaptive convolution kernel for artificial neural networks
(Academic Press Inc., 2021-02)
Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size ...
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07)
While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...
Unreasonable effectiveness of last hidden layer activations for adversarial robustness
(Institute of Electrical and Electronics Engineers Inc., 2022)
In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability ...
CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles
(IEEE, 2022-10)
A convolutional neural network (CNN) based deep learning (DL) technique for electromagnetic imaging of rough surfaces separating two dielectric media is presented. The direct scattering problem is formulated through the ...
Recovery of impenetrable rough surface profiles via CNN-based deep learning architecture
(Taylor and Francis Ltd., 2022-08-18)
In this paper, a convolutional neural network (CNN)-based deep learning (DL) architecture for the solution of an electromagnetic inverse problem related to imaging of the shape of the perfectly electric conducting (PEC) ...
El yazısı rakam sınıflandırması için gözetimsiz benzerlik tabanlı evrişimler
(Institute of Electrical and Electronics Engineers Inc., 2022)
Effective training of filters in Convolutional Neural Networks (CNN) ensures their success. In order to achieve good classification results in CNNs, filters must be carefully initialized, trained and fine-tuned. We propose ...
Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset
(IEEE, 2022-11-18)
Facial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, ...