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Toplam kayıt 11, listelenen: 1-10
Improved microphone array design with statistical speaker verification
(Elsevier Ltd, 2021-04)
Conventional microphone array implementations aim to lock onto a source with given location and if required, tracking it. It is a challenge to identify the intended source when the location of the source is unknown and ...
Unsupervised textile defect detection using convolutional neural networks
(Elsevier Ltd, 2021-12)
In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It ...
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
(Springer, 2022-03)
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ...
TENET: a new hybrid network architecture for adversarial defense
(Springer Science and Business Media Deutschland GmbH, 2023-08)
Deep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly ...
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. ...
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, ...
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 ...
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 ...
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 ...
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 ...