Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Louloudakis, Nikolaos"'
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework. However, this conversion process ma
Externí odkaz:
http://arxiv.org/abs/2312.15101
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent o
Externí odkaz:
http://arxiv.org/abs/2306.06157
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal
Externí odkaz:
http://arxiv.org/abs/2306.06208
The increased utilization of Artificial Intelligence (AI) solutions brings with it inherent risks, such as misclassification and sub-optimal execution time performance, due to errors introduced in their deployment infrastructure because of problemati
Externí odkaz:
http://arxiv.org/abs/2306.01697
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect
Externí odkaz:
http://arxiv.org/abs/2211.00471
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e74a77f2cbce44efe8f39eb81d36efb3
http://arxiv.org/abs/2306.06157
http://arxiv.org/abs/2306.06157
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10ed95dffd4809dfc18f243f99038eb3
http://arxiv.org/abs/2306.06208
http://arxiv.org/abs/2306.06208
Publikováno v:
2014 IEEE International Conference on Data Mining Workshop; 2014, p797-803, 7p
