Evaluating resilience of deep learning models
সংরক্ষণ করুন:
| লেখক: | , , |
|---|---|
| বিন্যাস: | artículo |
| প্রকাশনার তারিখ: | 2020 |
| বিবরন: | Deep learning applications have become a valuable tool to solve complex problems in many critical areas. It is important to provide reliability on the outputs of those applications, even if failures occur during execution. In this paper, we present a reliability evaluation of three deep learning models. We use an ImageNet dataset and a homebrew fault injector to make all the tests. The results show there is a difference in failure sensitivity among the models. Also, there are models that despite an increase in the failure rate can keep the resulting error values low. |
| দেশ: | Repositorio UNA |
| প্রতিষ্ঠান: | Universidad Nacional de Costa Rica |
| Repositorio: | Repositorio UNA |
| ভাষা: | Inglés |
| OAI Identifier: | oai:null:11056/26727 |
| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/11056/26727 https://doi.org/10.18845/tm.v33i5.5071 |
| মুখ্য শব্দ: | MODELOS APRENDIZAJE PROFUNDO (APRENDIZAJE AUTOMÁTICO) RESILIENCIA SEGURIDAD (INFORMÁTICA) INYECCIÓN DE FALLOS TOLERANCIA A FALLOS MODELS DEEP LEARNING (MACHINE LEARNING) RESILIENCE SECURITY (COMPUTING) FAULT INJECTION FAULT TOLERANCE |