TY - GEN
T1 - Adversarial VAE with Normalizing Flows for Multi-Dimensional Classification
AU - Zhang, Wenbo
AU - Gou, Yunhao
AU - Jiang, Yuepeng
AU - Zhang, Yu
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2022
Y1 - 2022
N2 - Exploiting correlations among class variables and using them to facilitate the learning process are a key challenge of Multi-Dimensional Classification (MDC) problems. Label embedding is an efficient strategy towards MDC problems. However, previous methods for MDC only use this technique as a way of feature augmentation and train a separate model for each class variable in MDC problems. Such two-stage approaches may cause unstable results and achieve suboptimal performance. In this paper, we propose an end-to-end model called Adversarial Variational AutoEncoder with Normalizing Flow (ADVAE-Flow), which encodes both features and class variables to probabilistic latent spaces. Specifically, considering the heterogeneity of class spaces, we introduce a normalizing flows module to increase the capacity of probabilistic latent spaces. Then adversarial training is adopted to help align transformed latent spaces obtained by normalizing flows. Extensive experiments on eight MDC datasets demonstrate the superiority of the proposed ADVAE-Flow model over state-of-the-art MDC models.
AB - Exploiting correlations among class variables and using them to facilitate the learning process are a key challenge of Multi-Dimensional Classification (MDC) problems. Label embedding is an efficient strategy towards MDC problems. However, previous methods for MDC only use this technique as a way of feature augmentation and train a separate model for each class variable in MDC problems. Such two-stage approaches may cause unstable results and achieve suboptimal performance. In this paper, we propose an end-to-end model called Adversarial Variational AutoEncoder with Normalizing Flow (ADVAE-Flow), which encodes both features and class variables to probabilistic latent spaces. Specifically, considering the heterogeneity of class spaces, we introduce a normalizing flows module to increase the capacity of probabilistic latent spaces. Then adversarial training is adopted to help align transformed latent spaces obtained by normalizing flows. Extensive experiments on eight MDC datasets demonstrate the superiority of the proposed ADVAE-Flow model over state-of-the-art MDC models.
KW - Multi-Dimensional Classification
KW - Normalizing flows
KW - VAE
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85142763283&origin=recordpage
U2 - 10.1007/978-3-031-18907-4_16
DO - 10.1007/978-3-031-18907-4_16
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-3-031-18906-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 219
BT - Pattern Recognition and Computer Vision
PB - Springer, Cham
T2 - 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022
Y2 - 4 November 2022 through 7 November 2022
ER -