| DC Field | Value | Language |
|---|
| dc.contributor.author | Pertsau, D. | - |
| dc.contributor.author | Lukashevich, M. | - |
| dc.contributor.author | Kupryianava, D. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-02-22T06:14:29Z | - |
| dc.date.available | 2024-02-22T06:14:29Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Pertsau, D. Compressing a convolution neural network based on quantization / D. Pertsau, M. Lukashevich, D. Kupryianava // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 269–272. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/54314 | - |
| dc.description.abstract | Modern deep neural network models contain a large number of parameters and have a significant size. In this
paper we experimentally investigate approaches to compression of convolutional neural network. The results showing the efficiency of quantization of the model while maintaining high accuracy are obtained. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | convolution neural network | en_US |
| dc.subject | quantization | en_US |
| dc.subject | quantization-aware training | en_US |
| dc.title | Compressing a convolution neural network based on quantization | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
|
