Add to ORKGA recent article in which it is claimed that adversarial examples exist in deep artificial neural networks (ANN) is critically examined. The newly discovered properties of ANNs are critically evaluated. Specifically, we point that adversarial examples can be serious problems in critical applications of pattern recognition. Also, they may stall the further development of artificial neural networks. We challenge the absolute existence of these examples, as this has not been universally proven yet. We also suggest that ANN structures, that correctly recognize adversarial examples, can be developed
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
Deep neural networks have achieved state-of-the-art performance in many artificial intelligence area...
Recent advancements in the field of deep learning have substantially increased the adoption rate of ...
Deep neural networks have been recently achieving high accuracy on many important tasks, most notabl...
In this paper, we study the adversarial examples existence and adversarial training from the standpo...
A wide range of defenses have been proposed to harden neural networks against adversarial attacks. H...
Deep learning technology achieves state of the art result in many computer vision missions. However,...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
Graduation date: 2017Access restricted to the OSU Community at author's request, from September 26, ...
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art resu...
Deep learning has become a pervasive tool in the field of machine learning, delivering unprecedented...
Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, ...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
Deep neural networks have achieved state-of-the-art performance in many artificial intelligence area...
Recent advancements in the field of deep learning have substantially increased the adoption rate of ...
Deep neural networks have been recently achieving high accuracy on many important tasks, most notabl...
In this paper, we study the adversarial examples existence and adversarial training from the standpo...
A wide range of defenses have been proposed to harden neural networks against adversarial attacks. H...
Deep learning technology achieves state of the art result in many computer vision missions. However,...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
Graduation date: 2017Access restricted to the OSU Community at author's request, from September 26, ...
Deep neural networks perform exceptionally well on various learning tasks with state-of-the-art resu...
Deep learning has become a pervasive tool in the field of machine learning, delivering unprecedented...
Deep neural networks (DNNs) provide excellent performance in image recognition, speech recognition, ...
Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigat...
Deep neural networks have achieved state-of-the-art performance in many artificial intelligence area...
Recent advancements in the field of deep learning have substantially increased the adoption rate of ...