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Toplam kayıt 6, listelenen: 1-6
Evrisimsel sinir ağları ile rekabetçi derin öğrenme
(Tübitak, 2021-09-15)
İmge üzerinde nesne tanımada, nesnenin imge üzerindeki yeri, açısal yönelimi ve ölçeği algoritmik olarak çözülmesi gereken önemli zorluklardır. Bu zorlukları aşmak için son yıllarda en sık kullanılan ve en başarılı sonuçları ...
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 ...
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 ...
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 ...
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 ...
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, ...