Capsule Neural Networks as Noise Stabilizer for Time Series Data
Soyeon Kim Jihyeon Seong Hyunkyung Han Jaesik Choi
Capsule Neural Networks utilize capsules, which bind neurons into a single
vector and learn position equivariant features, which makes them more robust
than original Convolutional Neural Networks. CapsNets employ an affine
transformation matrix and dynamic routing with coupling coefficients to learn
robustly. In this paper, we investigate the effectiveness of CapsNets in
analyzing highly sensitive and noisy time series sensor data. To demonstrate
CapsNets robustness, we compare their performance with original CNNs on
electrocardiogram data, a medical time series sensor data with complex patterns
and noise. Our study provides empirical evidence that CapsNets function as
noise stabilizers, as investigated by manual and adversarial attack experiments
using the fast gradient sign method and three manual attacks, including offset
shifting, gradual drift, and temporal lagging. In summary, CapsNets outperform
CNNs in both manual and adversarial attacked data. Our findings suggest that
CapsNets can be effectively applied to various sensor systems to improve their
resilience to noise attacks. These results have significant implications for
designing and implementing robust machine learning models in real world
applications. Additionally, this study contributes to the effectiveness of
CapsNet models in handling noisy data and highlights their potential for
addressing the challenges of noise data in time series analysis.