arXiv Open Access 2021

RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion

Joaquin Royo-Miquel Silvia Tolu Frederik E. T. Schöller Roberto Galeazzi

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Abstrak

The paper proposes a method to convert a deep learning object detector into an equivalent spiking neural network. The aim is to provide a conversion framework that is not constrained to shallow network structures and classification problems as in state-of-the-art conversion libraries. The results show that models of higher complexity, such as the RetinaNet object detector, can be converted with limited loss in performance.

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Penulis (4)

Joaquin Royo-Miquel

Silvia Tolu

Frederik E. T. Schöller

Roberto Galeazzi

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APA MLA BibTeX

Royo-Miquel, J., Tolu, S., Schöller, F.E.T., Galeazzi, R. (2021). RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion. https://arxiv.org/abs/2106.05624

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓