Two-stage Image Classification Supervised by a Single Teacher Single Student Model

By Jianhang Zhou, Shaoning Zeng, Bob Zhang. 2019

4 citations recorded by the source corpus.

The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel two-stage representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage image classification framework. We seek the nearest neighbours of the test sample to choose candidate target classes. Meanwhile, the first stage classifier is formulated as the teacher, which holds the classification scores. The samples of the candidate classes are utilized to learn a student classifier based on L2-minimization in the second stage. The student will be supervised by the teacher classifier, which approves the student only if it obtains a higher score. In actuality, the proposed framework generates a stronger classifier by staging two weaker classifiers in a novel way. The experiments conducted on several face and object databases show that our proposed framework is effective and outperforms multiple popular classification methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Knowledge Distillation: A Survey (2020, methodology): “…2019; Yang et al. 2020c; Tang et al. 2019; Hu et al. 2018; Sun et al. 2019; Nakashole and Flauger 2017; Jiao et al. 2020; Wang et al. 2018d; Zhou et al. 2019a; Sanh et al. 2019; Turc et al. 2019; Arora et al. 2019; Clark et al. 2019; Kim and Rush 2016; Mou et al. 2016; Liu et al. 2019f;…”