Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. The experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. This could also be a promising direction for fast training of face recognition models.
@article{arxiv.1605.02424,
title = {Learning Discriminative Features with Class Encoder},
author = {Hailin Shi and Xiangyu Zhu and Zhen Lei and Shengcai Liao and Stan Z. Li},
journal= {arXiv preprint arXiv:1605.02424},
year = {2016}
}
Comments
Accepted by CVPR2016 Workshop of Robust Features for Computer Vision