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pro vyhledávání: '"Chan, Philip K."'
Autor:
Jia, Jingyun, Chan, Philip K.
Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In this paper, we use a two-stage training strategy for the OSR problems. In
Externí odkaz:
http://arxiv.org/abs/2209.14385
Autor:
Jia, Jingyun, Chan, Philip K.
Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training proc
Externí odkaz:
http://arxiv.org/abs/2205.06918
Autor:
Jia, Jingyun, Chan, Philip K.
The objective of Open set recognition (OSR) is to learn a classifier that can reject the unknown samples while classifying the known classes accurately. In this paper, we propose a self-supervision method, Detransformation Autoencoder (DTAE), for the
Externí odkaz:
http://arxiv.org/abs/2105.13557
Autor:
Jia, Jingyun, Chan, Philip K.
Open set recognition (OSR) is the problem of classifying the known classes, meanwhile identifying the unknown classes when the collected samples cannot exhaust all the classes. There are many applications for the OSR problem. For instance, the freque
Externí odkaz:
http://arxiv.org/abs/2006.15117
Akademický článek
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Autor:
Chan, Philip K.
BACKGROUND: Among military personnel, mental health disorders are some of themost common and disabling medical conditions. Reports have suggested that National Guard soldiers are at an increased risk of developing psychiatric disorders as compared to
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=case1458924994
Autor:
Hassen, Mehadi, Chan, Philip K.
Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known c
Externí odkaz:
http://arxiv.org/abs/1802.04365
Akademický článek
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Autor:
Ranjbarrad, Samira1 (AUTHOR) samira.ranjbar@torontomu.ca, Chan, Philip K.1 (AUTHOR) p4chan@torontomu.ca
Publikováno v:
Polymers (20734360). Aug2023, Vol. 15 Issue 16, p3475. 22p.
Autor:
Ranjbarrad, Samira1 (AUTHOR), Chan, Philip K.1 (AUTHOR) p4chan@ryerson.ca
Publikováno v:
Polymers (20734360). Oct2022, Vol. 14 Issue 20, p4345-4345. 24p.