AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection

Unsupervised anomaly detection is coming into the spotlight these days in various practical domains due to the limited amount of anomaly data. One of the major approaches for it is a normalizing flow which pursues the invertible transformation of a complex distribution as images into an easy distribution as N(0, I). In fact, algorithms based on normalizing flow like FastFlow and CFLOW-AD establish state-of-the-art performance on unsupervised anomaly detection tasks. Nevertheless, we investigate these algorithms convert normal images into not N(0, I) as their destination, but an arbitrary normal distribution. Moreover, their performances are often unstable, which is highly critical for unsupervised tasks because data for validation are not provided. To break through these observations, we propose a simple solution AltUB which introduces alternating training to update the base distribution of normalizing flow for anomaly detection. AltUB effectively improves the stability of performance of normalizing flow. Furthermore, our method achieves the new state-of-the-art performance of the anomaly segmentation task on the MVTec AD dataset with 98.8% AUROC.Comment: 9 pages, 4 figure

GIST-AiTeR Speaker Diarization System for VoxCeleb Speaker Recognition Challenge (VoxSRC) 2023

This report describes the submission system by the GIST-AiTeR team for the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23) Track 4. Our submission system focuses on implementing diverse speaker diarization (SD) techniques, including ResNet293 and MFA-Conformer with different combinations of segment and hop length. Then, those models are combined into an ensemble model. The ResNet293 and MFA-Conformer models exhibited the diarization error rates (DERs) of 3.65% and 3.83% on VAL46, respectively. The submitted ensemble model provided a DER of 3.50% on VAL46, and consequently, it achieved a DER of 4.88% on the VoxSRC-23 test set.Comment: 2023 VoxSRC Track

Validation of the finger counting method using the Monte Carlo simulation

Purpose The dose of drug and the size of instrument are determined based on children’s weight. We aimed to validate the finger counting method (FCM) for weight estimation in Korean children using the Monte Carlo simulation. Methods We estimated the weight of Korean children aged 1 to 9 years by the FCM. These measurements were compared with the weight extracted by the Monte Carlo simulation applied to the “2007 Korean Children and Adolescents Growth Standard.” Pearson correlation coefficients (r) were measured to assess the correlation between the weight extracted by the simulation and that estimated by FCM. Bland-Altman analyses were performed to assess the agreement between the weight extracted by the simulation and that estimated by FCM and 2 other well-known pediatric weight estimation formulas (the Advanced Pediatric Life Support and Luscombe formulas). Results Data regarding 9,000 children’s weight selected by age and gender was randomly extracted using the simulation. We found a positive correlation between the weight estimated by the FCM and the weight extracted (in boys, r = 0.896, P < 0.001; in girls, r = 0.899, P < 0.001). The FCM tended to underestimate weight in the children aged 7 years or old. Conclusion This article suggests the usefulness of FCM in weight estimation, particularly in children younger than 7 years. With appreciation of the limitation in older children, the FCM could be applied to emergency practice