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An active unseen sample selection framework for generalized zero-shot classification

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Abstract

The purpose of generalized zero-shot classification (GZSC) is to classify the test samples no matter whether training classes (seen classes) or new classes (unseen classes) they are from. However, there are no labeled samples from new classes in the training process. Thus, selecting some unseen samples and labeling them are significant for GZSC. We present two novel ideas for GZSC: (1) splitting target samples into seen and unseen ones; (2) improving the GZSC performance with less manual annotations of the unseen samples. We propose an active unseen sample selection framework for GZSC tasks (AUSS). Specifically, a two-stage coarse-to-fine-grained selection method is first used to split target samples into seen and unseen ones. The selected unseen samples can be divided into high-confidence and informative ones. Unlike traditional active learning methods focusing on only the informative samples, we especially focus on the large number of high-confidence unseen samples. The high-confidence unseen samples are assigned with pseudo labels which do not need to be manually labeled. We select as few as possible informative unseen samples for manually labeling. Thanks to these high-confidence unseen samples and informative unseen samples, we do not need to train generative models for generating virtual unseen samples. Experiments on widely-adopted GZSC benchmarks demonstrate the advantages of AUSS over existing methods.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant no. 61806155, National Natural Science Foundation of shaanxi province (Grant No.2020GY-062, 2020JQ-323), China Postdoctoral Science Foundation funded project under Grant no. 2018M631125, Fundamental Research Funds for the Central Universities under Grant no. XJS200303, Nature Science Foundation of Anhui Province under Grant no. 1908085MF186.

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  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Xiao Li, Min Fang & Bo Chen

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  1. Xiao Li
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  2. Min Fang
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Correspondence to Min Fang.

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Li, X., Fang, M. & Chen, B. An active unseen sample selection framework for generalized zero-shot classification. Int. J. Mach. Learn. & Cyber. 13, 2119–2134 (2022). https://doi.org/10.1007/s13042-022-01509-7

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