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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SP, IPSJ-SLP, EA, SIP [detail] |
2023-03-01 14:45 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Personality Recognition on Dyadic Interactions with Representation Learning Nathania Nah (Tokyo Tech), Takafumi Koshinaka (YCU), Koichi Shinoda (Tokyo Tech) EA2022-117 SIP2022-161 SP2022-81 |
Personality computing explores methods of automatically measuring human traits to create a better understanding of the h... [more] |
EA2022-117 SIP2022-161 SP2022-81 pp.241-246 |
PRMU |
2022-09-15 10:45 |
Kanagawa |
(Primary: On-site, Secondary: Online) |
Effect validation of adversarial auxiliary classifier for video disentanglement Takeshi Haga, Hiroshi Kera, Kazuhiko Kawamoto (Chiba Univ) PRMU2022-21 |
The Disentanglement of sequential data such as video requires inductive biases to separate static latent variables from ... [more] |
PRMU2022-21 pp.67-71 |
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] |
2022-02-21 16:45 |
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] |
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NLP, NC (Joint) |
2020-01-24 10:10 |
Okinawa |
Miyakojima Marine Terminal |
Optimal Transport based Autoencoder for class and style Disentanglement Florian Tambon, Tetsuo Furukawa (Kyutech) NC2019-62 |
The Sinkhorn autoencoder is a novel generative model using optimal transport to model the aggregated posterior from samp... [more] |
NC2019-62 pp.17-22 |
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