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pro vyhledávání: '"Arya, Atrin"'
Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distribut
Externí odkaz:
http://arxiv.org/abs/2409.07351
Recent research has revealed that the security of deep neural networks that directly process 3D point clouds to classify objects can be threatened by adversarial samples. Although existing adversarial attack methods achieve high success rates, they d
Externí odkaz:
http://arxiv.org/abs/2110.03745
Autor:
Salehi, Mohammadreza, Arya, Atrin, Pajoum, Barbod, Otoofi, Mohammad, Shaeiri, Amirreza, Rohban, Mohammad Hossein, Rabiee, Hamid R.
Autoencoders (AE) have recently been widely employed to approach the novelty detection problem. Trained only on the normal data, the AE is expected to reconstruct the normal data effectively while fail to regenerate the anomalous data, which could be
Externí odkaz:
http://arxiv.org/abs/2003.05669
Autor:
Salehi, Mohammadreza, Arya, Atrin, Pajoum, Barbod, Otoofi, Mohammad, Shaeiri, Amirreza, Rohban, Mohammad Hossein, Rabiee, Hamid R.
Publikováno v:
In Neural Networks December 2021 144:726-736
