用知识蒸馏打造轻量表情识别模型，适合实时用户体验测试
Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design
- 用教师模型蒸馏出三类轻量化学生模型，通道数减半至八分之七
- 学生模型A在FER2013上达76.33%准确率，比EmoNeXt高0.21%
- 推理速度更快、内存占用更低，适合部署在资源受限设备
面部情绪识别在用户体验领域日益重要，尤其在现代可用性测试中，有助于深入理解用户满意度与参与度。本研究通过知识蒸馏框架扩展ResEmoteNet模型，开发出专为可用性测试设计的轻量级学生模型——Mini-ResEmoteNet。在FER2013和RAF-DB数据集上评估了三种学生模型架构：学生模型A、B、C，其每层特征通道数分别较教师模型减少约50%、75%和87.5%。结果显示，学生模型A（E1）在FER2013数据集上测试准确率达76.33%，较EmoNeXt提升0.21个百分点。同时，相较ResEmoteNet，该方法在推理速度和内存使用方面均有显著优化，性能超越其他先进方法。
原文摘要 · Abstract (English)
Facial Emotion Recognition has emerged as increasingly pivotal in the domain of User Experience, notably within modern usability testing, as it facilitates a deeper comprehension of user satisfaction and engagement. This study aims to extend the ResEmoteNet model by employing a knowledge distillation framework to develop Mini-ResEmoteNet models - lightweight student models - tailored for usability testing. Experiments were conducted on the FER2013 and RAF-DB datasets to assess the efficacy of three student model architectures: Student Model A, Student Model B, and Student Model C. Their development involves reducing the number of feature channels in each layer of the teacher model by approximately 50%, 75%, and 87.5%. Demonstrating exceptional performance on the FER2013 dataset, Student Model A (E1) achieved a test accuracy of 76.33%, marking a 0.21% absolute improvement over EmoNeXt. Moreover, the results exhibit absolute improvements in terms of inference speed and memory usage during inference compared to the ResEmoteNet model. The findings indicate that the proposed methods surpass other state-of-the-art approaches.
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