30 citations · 45 across the 8 of their papers we have counts for
8 papers
Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Time-sensitive machine learning benefits from Sequential Probability Ratio Test (SPRT), which provides an optimal stopping time for early classification of time series. However, in…
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to esti…
Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training
Yusuke Akamatsu, Yoshifumi Onishi, Hitoshi Imaoka +2
Edema is a common symptom of kidney disease, and quantitative measurement of edema is desired. This paper presents a method to estimate the degree of edema from facial images taken…
Blood Oxygen Saturation Estimation from Facial Video via DC and AC components of Spatio-temporal Map
Yusuke Akamatsu, Yoshifumi Onishi, Hitoshi Imaoka
Peripheral blood oxygen saturation (SpO2), an indicator of oxygen levels in the blood, is one of the most important physiological parameters. Although SpO2 is usually measured usin…
Joint Feature Distribution Alignment Learning for NIR-VIS and VIS-VIS Face Recognition
Takaya Miyamoto, Hiroshi Hashimoto, Akihiro Hayasaka +2
Face recognition for visible light (VIS) images achieve high accuracy thanks to the recent development of deep learning. However, heterogeneous face recognition (HFR), which is a f…
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
Akinori F. Ebihara, Taiki Miyagawa, Kazuyuki Sakurai +1
Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this…
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation
Makoto Takamoto, Yusuke Morishita, Hitoshi Imaoka
Compressing deep neural network (DNN) models becomes a very important and necessary technique for real-world applications, such as deploying those models on mobile devices. Knowled…
Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices
Akinori F. Ebihara, Kazuyuki Sakurai, Hitoshi Imaoka
In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here,…