论文标题
端到端的面部深度学习功能压缩，并提高教师
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement
论文作者
论文摘要
在本文中，我们通过利用深层神经网络的表示和学习能力来提出一种新颖的端到端特征压缩方案，并以有希望的准确性和效率来智能前端配备分析。特别是，提取的特征通过优化获得功能中特征表示的费率分数成本来以端到端的方式进行紧凑的编码。为了进一步提高压缩性能，我们提出了一个潜在的代码级别教师学生增强模型，该模型可以有效地将低比特率表示形式转移到高比特率。这种策略进一步使我们能够自适应地将表示成本转移到解码计算中，从而通过增强的解码功能导致更灵活的特征压缩。我们使用面部特征来验证所提出的模型的有效性，实验结果揭示了与现有模型相比，在速率准确性方面的压缩性能更好。
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.
