7 citations · 15 across the 12 of their papers we have counts for
12 papers
Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
Taiki Miyagawa, Akinori F. Ebihara
We propose non-parametric estimators for the average run length (ARL) and average detection delay (ADD) in quickest changepoint detection (QCD) under finite and irregular sequence…
Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models
Kosuke Kihara, Junki Mori, Taiki Miyagawa +1
Federated Learning (FL) offers a framework for training models collaboratively while preserving data privacy of each client. Recently, research has focused on Federated Source-Free…
Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images
Takuma Amada, Kazuya Kakizaki, Taiki Miyagawa +3
In this paper, we present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems. To ensure the reliability of eKYC systems against d…
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…
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
Junki Mori, Kosuke Kihara, Taiki Miyagawa +3
Federated learning (FL) commonly assumes that the server or some clients have labeled data, which is often impractical due to annotation costs and privacy concerns. Addressing this…
U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation
Yusuke Akamatsu, Akinori F. Ebihara, Terumi Umematsu
Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face videos captured by a camera, has th…
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…
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series
Azusa Sawada, Taiki Miyagawa, Akinori F. Ebihara +2
Time series data are often obtained only within a limited time range due to interruptions during observation process. To classify such partial time series, we need to account for 1…
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…
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
Taiki Miyagawa, Akinori F. Ebihara
We propose a model for multiclass classification of time series to make a prediction as early and as accurate as possible. The matrix sequential probability ratio test (MSPRT) is k…
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…
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,…