An efficient test method for noise robustness of deep neural networks

Autor:	Michio Yokoyama, Atushi Tanaka, Hironori Sakata, Seung-Il Cho, Muneki Yasuda, Tomochika Harada
Rok vydání:	2019
Předmět:	Computer science business.industry Robustness (computer science) Deep learning Electronic engineering Deep neural networks Test method Artificial intelligence business Effective algorithm
Zdroj:	Nonlinear Theory and Its Applications, IEICE. 10:221-235
ISSN:	2185-4106
DOI:	10.1587/nolta.10.221
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a12b2b611f063c537a0c0bf40f7895bf https://doi.org/10.1587/nolta.10.221 Zobrazit plný text záznamu