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Open Access

A Semi-Supervised Attention Model for Identifying Authentic Sneakers

National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.
Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Alibaba Company, Hangzhou 310000, China.
Rutgers University, New York, NJ 07102, USA.
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Abstract

To protect consumers and those who manufacture and sell the products they enjoy, it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one. The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification. In this paper, we develop a Semi-Supervised Attention (SSA) model to work in conjunction with a large-scale multiple-source dataset named YSneaker, which consists of sneakers from various brands and their authentication results, to identify authentic sneakers. Specifically, the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure. The model draws on the weighted average of the output feature representations, where the weights are determined by an additional shallow neural network. This allows the SSA model to focus on the most important images of a sneaker for use in identification. A unique feature of the SSA model is its ability to take advantage of unlabeled data, which can help to further minimize the intra-class variation for more discriminative feature embedding. To validate the model, we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies. The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate.

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Big Data Mining and Analytics
Pages 29-40
Cite this article:
Yang Y, Zhu N, Wu Y, et al. A Semi-Supervised Attention Model for Identifying Authentic Sneakers. Big Data Mining and Analytics, 2020, 3(1): 29-40. https://doi.org/10.26599/BDMA.2019.9020017

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Received: 21 May 2019
Accepted: 25 September 2019
Published: 19 December 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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