Search Results for author: Hideo Terada

Found 2 papers, 1 papers with code

Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss

no code implementations21 May 2021 Masaki Nakanishi, Kazuki Sato, Hideo Terada

Hence, to improve the accuracy of the reconstruction of high-frequency components, we introduce a new loss function named weighted frequency domain loss(WFDL).

Anomaly Detection

B-DCGAN:Evaluation of Binarized DCGAN for FPGA

1 code implementation29 Mar 2018 Hideo Terada, Hayaru Shouno

We are trying to implement deep neural networks in the edge computing environment for real-world applications such as the IoT(Internet of Things), the FinTech etc., for the purpose of utilizing the significant achievement of Deep Learning in recent years.

Binarization Edge-computing

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