提出改进版DARTS，自动搜索眼动识别网络结构，提升准确率与泛化能力
Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition
- 独立训练架构参数α，突破权重共享限制，搜索更精准
- 引入模块输入权重β，动态选择输入特征，缓解过拟合
- 在4个公开数据集上达到顶尖性能，适用于多特征时序任务
眼动生物识别是一种安全且创新的身份验证方法。深度学习虽表现优异，但其网络结构依赖人工设计与先验知识。为解决此问题，我们将自动化网络搜索（NAS）引入眼动识别领域，提出改进的Relax DARTS算法，以实现更高效的网络搜索与训练。核心思路是通过独立训练架构参数α，绕过权重共享问题，获得更精确的目标架构；同时引入模块输入权重β，使网络单元可灵活选择输入，缓解过拟合现象并提升性能。在四个公开数据库上的实验表明，Relax DARTS达到当前最优识别性能，且具备在其他多特征时序分类任务中的良好适应性。
原文摘要 · Abstract (English)
Eye movement biometrics is a secure and innovative identification method. Deep learning methods have shown good performance, but their network architecture relies on manual design and combined priori knowledge. To address these issues, we introduce automated network search (NAS) algorithms to the field of eye movement recognition and present Relax DARTS, which is an improvement of the Differentiable Architecture Search (DARTS) to realize more efficient network search and training. The key idea is to circumvent the issue of weight sharing by independently training the architecture parameters $α$ to achieve a more precise target architecture. Moreover, the introduction of module input weights $β$ allows cells the flexibility to select inputs, to alleviate the overfitting phenomenon and improve the model performance. Results on four public databases demonstrate that the Relax DARTS achieves state-of-the-art recognition performance. Notably, Relax DARTS exhibits adaptability to other multi-feature temporal classification tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。