Margin-Mix: Semi–Supervised Learning for Face Expression Recognition
Abstract
In this paper, as we aim to construct a semi-supervised learning algorithm, we exploit the characteristics of the Deep Convolutional Networks to provide, for an input image, both an embedding descriptor and a prediction. The unlabeled data is combined with the labeled one in order to provide synthetic data, which describes better the input space. The network is asked to provide a large margin between clusters, while new data is self-labeled by the distance to class centroids, in the embedding space. The method is tested on standard benchmarks for semi--supervised learning, where it matches state of the art performance and on the problem of face expression recognition where it increases the accuracy by a noticeable margin.
Cite
Text
Florea et al. "Margin-Mix: Semi–Supervised Learning for Face Expression Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58592-1_1Markdown
[Florea et al. "Margin-Mix: Semi–Supervised Learning for Face Expression Recognition." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/florea2020eccv-marginmix/) doi:10.1007/978-3-030-58592-1_1BibTeX
@inproceedings{florea2020eccv-marginmix,
title = {{Margin-Mix: Semi–Supervised Learning for Face Expression Recognition}},
author = {Florea, Corneliu and Badea, Mihai and Florea, Laura and Racoviteanu, Andrei and Vertan, Constantin},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58592-1_1},
url = {https://mlanthology.org/eccv/2020/florea2020eccv-marginmix/}
}