论文标题
分散的对抗自动编码器，用于主题不变的生理特征提取
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
论文作者
论文摘要
生物信号处理的最新发展使用户能够以可靠和安全的方式利用其生理状态来操纵设备。生理感知的一个主要挑战在于生物信号跨不同用户和任务的变异性。为了解决这个问题，我们提出了一个对抗性功能提取器，用于传输学习来利用分离的通用表示。我们通过引入其他对手和滋扰网络来考虑与任务相关的功能和用户歧视信息之间的权衡，以操纵潜在表示，以便学习的功能提取器适用于未知用户和各种任务。跨受试者转移评估的结果表现出所提出的框架的好处，平均分类准确性提高了8.8％，并证明了对更广泛的受试者的适应性。
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent representations such that the learned feature extractor is applicable to unknown users and various tasks. Results on cross-subject transfer evaluations exhibit the benefits of the proposed framework, with up to 8.8% improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.
