Search Results for author: Naoya Muramatsu

Found 2 papers, 1 papers with code

AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild

1 code implementation24 Mar 2021 Daniel Joska, Liam Clark, Naoya Muramatsu, Ricardo Jericevich, Fred Nicolls, Alexander Mathis, Mackenzie W. Mathis, Amir Patel

Animals are capable of extreme agility, yet understanding their complex dynamics, which have ecological, biomechanical and evolutionary implications, remains challenging.

3D Pose Estimation Animal Pose Estimation +1

Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification

no code implementations21 Feb 2021 Naoya Muramatsu, Hai-Tao Yu

With the continued innovations of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention owing to their low power consumption. However, for continuous data values, they must employ a coding process to convert the values to spike trains. Thus, they have not yet exceeded the performance of artificial neural networks (ANNs), which handle such values directly. To this end, we combine an ANN and an SNN to build versatile hybrid neural networks (HNNs) that improve the concerned performance. To qualify this performance, MNIST and CIFAR-10 image datasets are used for various classification tasks in which the training and coding methods changes. In addition, we present simultaneous and separate methods to train the artificial and spiking layers, considering the coding methods of each. We find that increasing the number of artificial layers at the expense of spiking layers improves the HNN performance. For straightforward datasets such as MNIST, it is easy to achieve the same performance as ANNs by using duplicate coding and separate learning. However, for more complex tasks, the use of Gaussian coding and simultaneous learning is found to improve the accuracy of HNNs while utilizing a smaller number of artificial layers.

General Classification Image Classification

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