# Semi-supervised learning method based on predefined evenly-distributed class centroids

arXiv: https://arxiv.org/abs/2001.04092 · HTML: https://mlsanity.com/paper/2001.04092/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Generation and frame characteristics of predefined evenly-distributed class centroids for pattern classification](https://mlsanity.com/paper/2105.00401/) (2021)
- [Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss](https://mlsanity.com/paper/2204.04665/) (2022)

## Abstract (arXiv)

Compared to supervised learning, semi-supervised learning reduces the dependence of deep learning on a large number of labeled samples. In this work, we use a small number of labeled samples and perform data augmentation on unlabeled samples to achieve image classification. Our method constrains all samples to the predefined evenly-distributed class centroids (PEDCC) by the corresponding loss function. Specifically, the PEDCC-Loss for labeled samples, and the maximum mean discrepancy loss for unlabeled samples are used to make the feature distribution closer to the distribution of PEDCC. Our method ensures that the inter-class distance is large and the intra-class distance is small enough to make the classification boundaries between different classes clearer. Meanwhile, for unlabeled samples, we also use KL divergence to constrain the consistency of the network predictions between unlabeled and augmented samples. Our semi-supervised learning method achieves the state-of-the-art results, with 4000 labeled samples on CIFAR10 and 1000 labeled samples on SVHN, and the accuracy is 95.10% and 97.58% respectively.