WCSE

FLPD-GANS: Fake License Plate Discrimination Gans for Generalized Zero-Shot Learning

Abstract—Most current generalized zero-shot learning (GZSL) methods need sufficient labels and other auxiliary information to obtain great results. In this paper, we propose Fake License Plate Discrimination GANs (FLPD-GANs) and introduce the first publicly available New Energy License Plate (NELP) image dataset named CCPONECD. Applied in the license plate (LP) image binary classification task, FLPD-GANs only need a binary label for training and can address the strong bias problem in GZSL tasks. CCPONECD contains nearly 7k unique new energy vehicles images and provides detailed LP vertex location annotations. In our work, the seen class is only real NELP image and the unseen class is manufactured fake NELP image. Trained with merely real NELP images, our FLPD-GANs can greatly discriminate between real and fake NELP images. Extensive experiments demonstrate that our FLPD-GANs model has 97.7% accuracy and performs well in NELP image discrimination for GZSL task

Index Terms—GZSL, image classification, GANs, license plate discrimination

Huaiyao Zhang, Yi Zhang, Caixin Zhu
School of Software Engineering, University of Science and Technology of China, CHINA
School of Computer Science and Technology, University of Science and Technology of China, CHINA

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Cite: Huaiyao Zhang, Yi Zhang, Caixin Zhu , " FLPD-GANS: Fake License Plate Discrimination Gans for Generalized Zero-Shot Learning " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 157-164, Shanghai, China, 19-21 June, 2020.