论文标题
通用生理代表性学习，使用软透明的无重量自动编码器
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
论文作者
论文摘要
人类的计算机互动（HCI）涉及技术的多学科融合，通过该技术可以通过监测用户的生理状态来控制外部设备。但是，由于身体/精神状况不稳定和任务促进活动，生理生物信号通常在用户和记录会话中有所不同。为了应对这一挑战，我们提出了一种用无重量自动编码器（RAE）概念编码的对抗特征的方法，以利用散布，刺激性和普遍表示。通过采用其他对抗性网络，我们利用潜在表示的随机分解，在特定于用户和任务之间的功能之间实现了良好的权衡。所提出的模型适用于更广泛的未知用户和任务以及不同的分类器。跨受试者转移评估的结果显示了所提出的框架的优势，平均主题转移分类精度提高了11.6％。
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.
