Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

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The main contribution of this paper is a simple semi- supervised pipeline that only uses the original training se t without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regu- larization for outliers (LSRO). This method assigns a uni- form label distribution to the unlabeled images, which reg- ularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retriev e a query person from other cameras. We adopt the deep con- volutional generative adversarial network (DCGAN) for sample generation, anda baseline convolutionalneuralnet - work (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% im- provement over a strong baseline
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