| Item type |
Symposium(1) |
| 公開日 |
2019-01-17 |
| タイトル |
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|
タイトル |
DeepSaucer: Verification Environment for Deep Neural Networks |
| タイトル |
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言語 |
en |
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タイトル |
DeepSaucer: Verification Environment for Deep Neural Networks |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
深層学習システムのテスト・検証 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
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資源タイプ |
conference paper |
| 著者所属 |
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株式会社日立製作所研究開発グループ |
| 著者所属 |
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株式会社日立製作所研究開発グループ |
| 著者所属 |
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株式会社日立製作所研究開発グループ |
| 著者所属 |
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株式会社日立製作所研究開発グループ |
| 著者所属 |
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株式会社日立製作所研究開発グループ |
| 著者所属 |
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サザンプトン大学エレクトロニクスアンドコンピュータサイエンス学部 |
| 著者所属 |
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サザンプトン大学エレクトロニクスアンドコンピュータサイエンス学部 |
| 著者所属(英) |
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en |
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Research & Development Group, Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Research & Development Group, Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Research & Development Group, Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Research & Development Group, Hitachi, Ltd. |
| 著者所属(英) |
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en |
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Research & Development Group, Hitachi, Ltd. |
| 著者所属(英) |
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|
en |
|
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School of Electronics and Computer Science, University of Southampton |
| 著者所属(英) |
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|
en |
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School of Electronics and Computer Science, University of Southampton |
| 著者名 |
佐藤, 直人
來間, 啓伸
金子, 昌永
中川, 雄一郎
小川, 秀人
ホン, タイソン
バトラー, マイケル
|
| 著者名(英) |
Naoto, Sato
Kuruma, Hironobu
Kaneko, Masanori
Nakagawa, Yuichiroh
Ogawa, Hideto
Thai, Son Hoang
Michael, Butler
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| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In recent years, a number of methods for verifying DNNs have been developed. Because the approaches of the methods differ and have their own limitations, we think that a number of verification methods should be applied to a developed DNN. To apply a number of methods to the DNN, it is necessary to translate either the implementation of the DNN or the verification method so that one runs in the same environment as the other. Since those translations are time-consuming, a utility tool, named DeepSaucer, which helps to retain and reuse implementations of DNNs, verification methods, and their environments, is proposed. In DeepSaucer, code snippets for loading DNNs, running verification methods, and creating their environments are retained and reused as software assets in order to reduce the cost of verifying DNNs. The feasibility of DeepSaucer is confirmed by implementing it on the basis of Anaconda, which provides a virtual environment for loading a DNN and running a verification method. In addition, the effectiveness of DeepSaucer is demonstrated by an use case example. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
In recent years, a number of methods for verifying DNNs have been developed. Because the approaches of the methods differ and have their own limitations, we think that a number of verification methods should be applied to a developed DNN. To apply a number of methods to the DNN, it is necessary to translate either the implementation of the DNN or the verification method so that one runs in the same environment as the other. Since those translations are time-consuming, a utility tool, named DeepSaucer, which helps to retain and reuse implementations of DNNs, verification methods, and their environments, is proposed. In DeepSaucer, code snippets for loading DNNs, running verification methods, and creating their environments are retained and reused as software assets in order to reduce the cost of verifying DNNs. The feasibility of DeepSaucer is confirmed by implementing it on the basis of Anaconda, which provides a virtual environment for loading a DNN and running a verification method. In addition, the effectiveness of DeepSaucer is demonstrated by an use case example. |
| 書誌情報 |
ウィンターワークショップ2019・イン・福島飯坂 論文集
巻 2019,
p. 7-8,
発行日 2019-01-17
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| 出版者 |
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言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-WWS2019004.pdf (230.1 kB)
