4 citations · 13 across the 6 of their papers we have counts for
8 papers
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks
Kentaro Ohno, Sekitoshi Kanai, Yasutoshi Ida
Gate functions in recurrent models, such as an LSTM and GRU, play a central role in learning various time scales in modeling time series data by using a bounded activation function…
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain
Shin'ya Yamaguchi, Sekitoshi Kanai
Generative adversarial networks built from deep convolutional neural networks (GANs) lack the ability to exactly replicate the high-frequency components of natural images. To allev…
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Masanori Yamada, Sekitoshi Kanai, Tomoharu Iwata +4
Adversarial training is actively studied for learning robust models against adversarial examples. A recent study finds that adversarially trained models degenerate generalization p…
Constraining Logits by Bounded Function for Adversarial Robustness
Sekitoshi Kanai, Masanori Yamada, Shin'ya Yamaguchi +2
We propose a method for improving adversarial robustness by addition of a new bounded function just before softmax. Recent studies hypothesize that small logits (inputs of softmax)…
Effective Data Augmentation with Multi-Domain Learning GANs
Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda
For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously…
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Sekitoshi Kanai, Yasutoshi Ida, Yasuhiro Fujiwara +2
We propose Absum, which is a regularization method for improving adversarial robustness of convolutional neural networks (CNNs). Although CNNs can accurately recognize images, rece…