no code implementations • 15 Apr 2024 • Masahito Toba, Seiichi Uchida, Hideaki Hayashi
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate confidence is important for successful PL.
no code implementations • 24 Mar 2024 • Yicheng Deng, Hideaki Hayashi, Hajime Nagahara
In this paper, we propose a Multi-Scale Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for facial expression spotting.
no code implementations • 21 Jun 2023 • Naoya Yasukochi, Hideaki Hayashi, Daichi Haraguchi, Seiichi Uchida
There are various font styles in the world.
no code implementations • 10 May 2023 • Hideaki Hayashi
In this paper, we propose a method to train a hybrid of discriminative and generative models in a single neural network (NN), which exhibits the characteristics of both models.
no code implementations • 5 Aug 2022 • Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida
This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation.
no code implementations • 6 Nov 2021 • Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.
1 code implementation • 17 Mar 2021 • Takato Otsuzuki, Heon Song, Seiichi Uchida, Hideaki Hayashi
As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data.
no code implementations • 26 Feb 2021 • Shohei Kubota, Hideaki Hayashi, Tomohiro Hayase, Seiichi Uchida
The interpretability of neural networks (NNs) is a challenging but essential topic for transparency in the decision-making process using machine learning.
no code implementations • 27 Sep 2020 • Masaki Yamagata, Hideaki Hayashi, Seiichi Uchida
In this paper, we propose a temporal prediction model that can deal with this bifurcation structure.
no code implementations • 6 May 2020 • Takato Otsuzuki, Hideaki Hayashi, Yuchen Zheng, Seiichi Uchida
This means that max pooling is too flexible to compensate for actual deformations.
no code implementations • 14 Nov 2019 • Hideaki Hayashi, Taro Shibanoki, Toshio Tsuji
In this study, a discriminative model based on the multivariate Johnson $S_\mathrm{U}$ translation system is transformed into a linear combination of coefficients and input vectors using log-linearization.
no code implementations • 14 Nov 2019 • Hideaki Hayashi, Taro Shibanoki, Keisuke Shima, Yuichi Kurita, Toshio Tsuji
This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns.
no code implementations • ICLR 2021 • Hideaki Hayashi, Seiichi Uchida
We propose a sparse classifier based on a discriminative GMM, referred to as a sparse discriminative Gaussian mixture (SDGM).
no code implementations • 25 Sep 2019 • Hideaki Hayashi, Seiichi Uchida
In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.
no code implementations • 14 Jun 2019 • Taichi Sumi, Brian Kenji Iwana, Hideaki Hayashi, Seiichi Uchida
This research attempts to construct a network that can convert online and offline handwritten characters to each other.
no code implementations • 31 May 2019 • Changhee Han, Leonardo Rundo, Ryosuke Araki, Yudai Nagano, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi
In this context, Generative Adversarial Networks (GANs) can synthesize realistic/diverse additional training images to fill the data lack in the real image distribution; researchers have improved classification by augmenting data with noise-to-image (e. g., random noise samples to diverse pathological images) or image-to-image GANs (e. g., a benign image to a malignant one).
no code implementations • 30 May 2019 • 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.
1 code implementation • 29 May 2019 • Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida
In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector.
1 code implementation • 26 May 2019 • Kumar Shridhar, Joonho Lee, Hideaki Hayashi, Purvanshi Mehta, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida, Sheraz Ahmed, Andreas Dengel
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
no code implementations • 17 May 2019 • Shota Harada, Hideaki Hayashi, Seiichi Uchida
GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data. Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled.
no code implementations • 29 Mar 2019 • Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original images; however, those transformed images intrinsically have a similar distribution to the original ones, leading to limited performance improvement.