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pro vyhledávání: '"Inkawhich, Matthew"'
Autor:
Inkawhich, Matthew, Inkawhich, Nathan, Yang, Hao, Zhang, Jingyang, Linderman, Randolph, Chen, Yiran
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work in open object detection today is disjointed and fails to adequately ad
Externí odkaz:
http://arxiv.org/abs/2404.10865
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail to provide high recall in open-world environments where important novel
Externí odkaz:
http://arxiv.org/abs/2208.11050
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks. As computational resources improve, a great deal of effort has been placed in effic
Externí odkaz:
http://arxiv.org/abs/2103.09891
Autor:
Inkawhich, Nathan, Liang, Kevin J, Wang, Binghui, Inkawhich, Matthew, Carin, Lawrence, Chen, Yiran
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decision boundaries at the output layer of the source model, our method per
Externí odkaz:
http://arxiv.org/abs/2004.14861
Adversarial attacks have exposed a significant security vulnerability in state-of-the-art machine learning models. Among these models include deep reinforcement learning agents. The existing methods for attacking reinforcement learning agents assume
Externí odkaz:
http://arxiv.org/abs/1905.11832
The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains. However, recent work has shown that these largely uninterpreta
Externí odkaz:
http://arxiv.org/abs/1811.11875
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail to provide high recall in open-world environments where important novel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::041595d5b1cfc98005c9a81372b8c1f1
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