Adversarial VAE with Normalizing Flows for Multi-Dimensional Classification

Zhang，Wenbo1; Gou，Yunhao2,3; Jiang，Yuepeng4; Zhang，Yu2,5

10.1007/978-3-031-18907-4_16

2022

0302-9743

1611-3349

LECT NOTES ARTIF INT

13534 LNCS

205-219

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.

Multi-Dimensional Classification Normalizing flows VAE

通讯

英语

2-s2.0-85142763283

Scopus

1.University of California Irvine,Irvine,United States
2.Southern University of Science and Technology,Shenzhen,China
3.Hong Kong University of Science and Technology,Hong Kong
4.City University of Hong Kong,Hong Kong
5.Peng Cheng Laboratory,Shenzhen,China

Zhang，Wenbo,Gou，Yunhao,Jiang，Yuepeng,et al. Adversarial VAE with Normalizing Flows for Multi-Dimensional Classification[C],2022:205-219.