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Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples


Because it is very expensive to collect a large number of labeled samples to train deep neural networks in certain fields, semi-supervised learning (SSL) researcher has become increasingly important in recent years. There are many consistency regularization-based methods for solving SSL tasks, such as the \(\Pi \) model and mean teacher. In this paper, we first show through an experiment that the traditional consistency-based methods exist the following two problems: (1) as the size of unlabeled samples increases, the accuracy of these methods increases very slowly, which means they cannot make full use of unlabeled samples. (2) When the number of labeled samples is vary small, the performance of these methods will be very low. Based on these two findings, we propose two methods, metric learning clustering (MLC) and auxiliary fake samples, to alleviate these problems. The proposed methods achieve state-of-the-art results on SSL benchmarks. The error rates are 10.20%, 38.44% and 4.24% for CIFAR-10 with 4000 labels, CIFAR-100 with 10,000 labels and SVHN with 1000 labels by using MLC. For MNIST, the auxiliary fake samples method shows great results in cases with the very few labels.

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This work was supported in part by the Natural Science Foundation of China under Grants 61876219, 61503144, 61761130081 and 61821003; in part by the National Key R&D Program of China under Grant 2017YFC1501301; and in part by the Fundamental Research Funds for the Central Universities (WUT: 2020III044).

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Correspondence to Cheng Lian.

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Zhou, W., Lian, C., Zeng, Z. et al. Improve Semi-supervised Learning with Metric Learning Clusters and Auxiliary Fake Samples. Neural Process Lett 53, 3427–3443 (2021).

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  • Semi-supervised learning
  • Metric learning
  • Variational auto-encoders
  • Very few labeled data