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Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent tr…
Statistical Test for Anomaly Detections by Variational Auto-Encoders
Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy +2
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in…
Statistical Test for Attention Map in Vision Transformer
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka +3
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among…
Selective Inference for Changepoint detection by Recurrent Neural Network
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its…
Bounded P-values in Parametric Programming-based Selective Inference
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inference…