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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 10 of 10  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2023-12-20
16:25
Tokyo National Institute of Informatics (Tokyo, Online)
(Primary: On-site, Secondary: Online)
Anomaly detection by deep support data descriptions with pseudo-anomaly data
Shuta Tsuchio, Takuya Kitamura (NIT, Toyama college) IBISML2023-34
This paper presents deep support vector data description (DSVDD) with pseudo-anomaly data that generated by generative m... [more] IBISML2023-34
pp.25-30
AI 2023-09-12
15:35
Hokkaido (Hokkaido) Variational Autoencoder Oriented Protection for Intellectual Property
Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) AI2023-31
In recent years, generative AI, which generates images based on instructions in natural language, has developed rapidly ... [more] AI2023-31
pp.180-186
SIP 2022-08-25
13:21
Okinawa Nobumoto Ohama Memorial Hall (Ishigaki Island) (Okinawa, Online)
(Primary: On-site, Secondary: Online)
Style Feature Extraction by Contrastive Learning and Mutual Information Constraints
Suguru Yasutomi, Toshihisa Tanaka (TUAT) SIP2022-52
Extracting style features is crucial for analyzing data. This paper proposes a style feature extraction using variationa... [more] SIP2022-52
pp.13-18
EA, SIP, SP, IPSJ-SLP [detail] 2022-03-01
14:45
Okinawa (Okinawa, Online)
(Primary: On-site, Secondary: Online)
Target speaker extraction based on conditional variational autoencoder and directional information in underdetermined condition
Rui Wang, Li Li, Tomoki Toda (Nagoya Univ) EA2021-76 SIP2021-103 SP2021-61
This paper deals with a dual-channel target speaker extraction problem in underdetermined conditions. A blind source sep... [more] EA2021-76 SIP2021-103 SP2021-61
pp.76-81
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
16:45
Online Online (Online) A Note on Disentanglement Using Deep Generative Model Based on Variational Autoencoder -- Introduction of Regularization Losses Based on Metrics of Disentangled Representation --
Nao Nakagawa, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
In this paper, we study disentangled representation learning using a deep generative model based on Variational Autoenco... [more]
R 2021-06-12
14:25
Online Online (Zoom) (Online) A Study on Dual-task VAE with Weibull distribution for RUL estimation and application to Aero-Propulsion System data
Ryosuke Sato, Mitsuhiro Kimura (Hosei Univ.) R2021-12
Remaining Useful Life (RUL) is one of the most important assessment measures in reliability engineering.
Although sever... [more]
R2021-12
pp.7-12
SP, EA, SIP 2020-03-03
09:00
Okinawa Okinawa Industry Support Center (Okinawa)
(Cancelled but technical report was issued)
Semi-supervised Self-produced Speech Enhancement and Suppression Based on Joint Source Modeling of Air- and Body-conducted Signals Using Variational Autoencoder
Shogo Seki, Moe Takada, Kazuya Takeda, Tomoki Toda (Nagoya Univ.) EA2019-140 SIP2019-142 SP2019-89
This paper proposes a semi-supervised method for enhancing and suppressing self-produced speech, using a variational aut... [more] EA2019-140 SIP2019-142 SP2019-89
pp.225-230
KBSE, SC 2019-11-08
13:30
Nagano Shinshu University (Nagano) Malicious Domain Names Detection Based on TF-IDE and Variational Autoencoder: Classification with Quantum-enhanced Support Vector Machine
Yuwei Sun (UTokyo), Ng S. T. Chong (UNU), Hideya Ochiai (UTokyo) KBSE2019-26 SC2019-23
With the development of network technology, the use of domain name system (DNS) becomes common. However, the attacks on ... [more] KBSE2019-26 SC2019-23
pp.19-23
SIP, EA, SP, MI
(Joint) [detail]
2018-03-19
10:25
Okinawa   (Okinawa) Non-parallel and Many-to-Many Voice Conversion Using Variational Autoencoder Conditioned by Phonetic Posteriorgrams and d-vectors
Yuki Saito (NTT/Univ. of Tokyo), Yusuke Ijima, Kyosuke Nishida (NTT), Shinnosuke Takamichi (Univ. of Tokyo) EA2017-105 SIP2017-114 SP2017-88
This paper proposes novel frameworks for non-parallel and many-to-many voice conversion (VC) using variational autoencod... [more] EA2017-105 SIP2017-114 SP2017-88
pp.21-26
PRMU, BioX 2018-03-18
16:10
Tokyo (Tokyo) Toward image inbetweening using Latent Model
Paulino Cristovao (Univ. of Tsukuba), Yusuke Tanimura, Hidemoto Nakada, Hideki Asoh (AIST) BioX2017-49 PRMU2017-185
Image interpolation is a well known problem in computer vision. Many approaches are restricted to optical flow and convo... [more] BioX2017-49 PRMU2017-185
pp.79-84
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