Learning Discriminative Features with Class Encoder

TL;DR

This paper introduces a class-encoder that leverages supervised information to learn discriminative features by reconstructing samples within the same class, improving classification and face recognition performance.

Contribution

It proposes a novel class-encoder formulation that minimizes intra-class variations and integrates with softmax for enhanced supervised training.

Findings

Improves classification accuracy on benchmark datasets.

Enhances face recognition performance.

Facilitates faster training of face recognition models.

Abstract

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.