TY - GEN
T1 - Modality conversion of handwritten patterns by cross variational autoencoders
AU - Sumi, Taichi
AU - Iwana, Brian Kenji
AU - Hayashi, Hideaki
AU - Uchida, Seiichi
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP17H06100.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.
AB - This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.
UR - http://www.scopus.com/inward/record.url?scp=85079879966&partnerID=8YFLogxK
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U2 - 10.1109/ICDAR.2019.00072
DO - 10.1109/ICDAR.2019.00072
M3 - Conference contribution
AN - SCOPUS:85079879966
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 407
EP - 412
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
PB - IEEE Computer Society
T2 - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
Y2 - 20 September 2019 through 25 September 2019
ER -