- Tytuł:
- Towards ensuring software interoperability between deep learning frameworks
- Autorzy:
-
Lee, Youn Kyu
Park, Seong Hee
Lim, Min Young
Lee, Soo-Hyun
Jeong, Jongwook - Powiązania:
- https://bibliotekanauki.pl/articles/23944833.pdf
- Data publikacji:
- 2023
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
deep learning
interoperability
validation
verification
deep learning framework
model conversion - Opis:
- With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 215--228
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł