- 著者
- Chisato Takahashi Kenya Jin'no
- 出版者
- Research Institute of Signal Processing, Japan
- 雑誌
- Journal of Signal Processing (ISSN:13426230)
- 巻号頁・発行日
- vol.27, no.4, pp.65-68, 2023-07-01 (Released:2023-07-01)
- 参考文献数
- 5
Neural Architecture Search (NAS), which aims to automatically optimize the structure of a neural network for achieving excellent classification performance, has attracted considerable attention in recent years. Recently, zero-shot evaluation methods have been proposed for estimating classification performance without training to reduce the search time. However, these indices are still insufficient for finding the best-performing neural networks. In this study, we demonstrate that it is possible to evaluate convolutional neural networks (CNNs) using the robustness of the rectified linear unit (ReLU) output distribution to weights. We propose a new zero-shot CNN evaluation index based on this robustness index.
- 著者
- Taichi Fukawa Kenya Jin'no
- 出版者
- The Institute of Electronics, Information and Communication Engineers
- 雑誌
- Nonlinear Theory and Its Applications, IEICE (ISSN:21854106)
- 巻号頁・発行日
- vol.13, no.2, pp.277-281, 2022 (Released:2022-04-01)
- 参考文献数
- 15
For an indefinite length spectrogram sequence of phonemes, we experimentally verified two methods of obtaining speaker embedding by transforming it to fixed length: adding padding and time stretching. We confirmed that both methods can maintain the extraction performance. We also confirm that the fixed frame length does not affect the results.
- 著者
- Yuki Hoshino Kenya Jin'no
- 出版者
- Research Institute of Signal Processing, Japan
- 雑誌
- Journal of Signal Processing (ISSN:13426230)
- 巻号頁・発行日
- vol.22, no.4, pp.153-156, 2018-07-25 (Released:2018-07-25)
- 参考文献数
- 15
- 被引用文献数
- 1
Recently, machine learning has been attracting attention. Machine learning is mainly realized by the learning of artificial neural networks. Various learning methods have been proposed; however, the learning methods are based on gradient methods. On the other hand, swarm intelligence (SI) algorithms have been attracting attention in the optimization field. Generally speaking, SI algorithms have a large computation cost. Therefore, there are few cases of SI algorithms being applied to machine learning. In this paper, we propose a novel learning algorithm for an artificial neural network which applies our proposed nonlinear map optimization (NMO) method. NMO consists of some simple particles which are driven by a simple nonlinear map. NMO can be classified as an SI algorithm. However, it has only a small computation cost. Therefore, NMO can be applied to a learning algorithm for an artificial neural network. In this paper, we introduce NMO, and a small learning simulation is carried out to confirm the performance of our learning method.