End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement
This addresses efficient facial analysis for intelligent front-end systems, though it appears incremental as it builds on existing compression methods.
The paper tackles facial feature compression by proposing an end-to-end deep learning scheme with teacher-student enhancement, achieving better rate-accuracy performance compared to existing models.
In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency. In particular, the extracted features are compactly coded in an end-to-end manner by optimizing the rate-distortion cost to achieve feature-in-feature representation. In order to further improve the compression performance, we present a latent code level teacher-student enhancement model, which could efficiently transfer the low bit-rate representation into a high bit rate one. Such a strategy further allows us to adaptively shift the representation cost to decoding computations, leading to more flexible feature compression with enhanced decoding capability. We verify the effectiveness of the proposed model with the facial feature, and experimental results reveal better compression performance in terms of rate-accuracy compared with existing models.