A Semi-Supervised Attention Model for Identifying Authentic Sneakers - Yang Yang; Nengjun Zhu; Yifeng Wu; Jian Cao; Dechuan Zhan; Hui Xiong (Big Data Mining and Analytics)
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.