31 citations · 83 across the 16 of their papers we have counts for
8 papers · 1 filter
A Discriminative Gaussian Mixture Model with Sparsity
Hideaki Hayashi, Seiichi Uchida
In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space.…
Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders
Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi +1
This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encode…
A Trainable Multiplication Layer for Auto-correlation and Co-occurrence Extraction
Hideaki Hayashi, Seiichi Uchida
In this paper, we propose a trainable multiplication layer (TML) for a neural network that can be used to calculate the multiplication between the input features. Taking an image a…
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida
In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a s…
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
Shota Harada, Hideaki Hayashi, Seiichi Uchida
The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning…
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +5
Convolutional Neural Networks (CNNs) achieve excellent computer-assisted diagnosis with sufficient annotated training data. However, most medical imaging datasets are small and fra…
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
Kumar Shridhar, Joonho Lee, Hideaki Hayashi +6
Activation functions play an important role in training artificial neural networks. The majority of currently used activation functions are deterministic in nature, with their fixe…
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +4
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transf…