Class-aware Variational Auto-encoder for Open Set Recognition

Compared with traditional classification models trained under the closed world assumption, Open Set Recognition (OSR) requires accurate classification for known classes as well as rejection for unknown ones. By modeling the distribution of each known class, Conditional Variational Auto-encoder (CVAE...

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Bibliographic Details
Main Authors: Wang, Ruofan, Guo, Jiayu, Zhao, Rui-Wei, Su, Ling, Ye, Yingzi, Zhang, Xiaobo, Zhang, Yuejie, Feng, Rui
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
class activation mapping
Codes
Image reconstruction
Image synthesis
open set recognition
Task analysis
variational auto-encoder
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Summary:Compared with traditional classification models trained under the closed world assumption, Open Set Recognition (OSR) requires accurate classification for known classes as well as rejection for unknown ones. By modeling the distribution of each known class, Conditional Variational Auto-encoder (CVAE) has achieved great success in OSR, even though it was originally proposed for image generation. In this paper, we propose a novel two-stage learning framework, Class-aware Variational Auto-encoder (CA-VAE) to better adapt CVAE to the OSR task. Pre-derived attention images are taken as the objective target for reconstruction, thus model is implicitly directed to focus on the class-discriminative regions of the image. In this way, the learned latent representation is de-biased towards class-aware. Experiments on standard image datasets demonstrate the outperformance of the proposed method over existing ones, which achieves new state-of-the-art results. Codes are available at https://github.com/roywang021/CA-VAE.
ISSN:1945-788X
DOI:10.1109/ICME55011.2023.00053