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.

CapsNets 在分析高敏感、噪音时间序列传感器数据方面具有良好的鲁棒性，通过与原始 CNNs 在心电图数据上的性能比较，证明了其噪音稳定化的功能和優越性。该研究结果对设计和实施鲁棒的机器学习模型在实际应用中具有重要意义，并强调了 CapsNet 模型在处理噪音数据方面的潜力和对时序分析中的噪音数据挑战的应对能力。

胶囊神经网络作为时间序列数据的噪音稳定器

Capsule Neural Networks as Noise Stabilizer for Time Series Data

Adversarial examples raise questions about whether neural network models are
sensitive to the same visual features as humans. In this paper, we first detect
adversarial examples or otherwise corrupted images based on a class-conditional
reconstruction of the input. To specifically attack our detection mechanism, we
propose the Reconstructive Attack which seeks both to cause a misclassification
and a low reconstruction error. This reconstructive attack produces undetected
adversarial examples but with much smaller success rate. Among all these
attacks, we find that CapsNets always perform better than convolutional
networks. Then, we diagnose the adversarial examples for CapsNets and find that
the success of the reconstructive attack is highly related to the visual
similarity between the source and target class. Additionally, the resulting
perturbations can cause the input image to appear visually more like the target
class and hence become non-adversarial. This suggests that CapsNets use
features that are more aligned with human perception and have the potential to
address the central issue raised by adversarial examples.

本文主要研究通过对输入进行类别有条件的重构来检测对抗样本，使用重构攻击来进攻检测机制，并发现 CapsNets 总是表现更好，进一步诊断 CapsNets 的对抗样本，发现了视觉相似性与攻击成功率之间的高度相关性。

使用类条件胶囊重构技术检测和诊断对抗性图像

Detecting and Diagnosing Adversarial Images with Class-Conditional  Capsule Reconstructions

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in
representation learning and are widely used in various text mining tasks such
as large-scale multi-label text classification. However, most existing deep
models for multi-label text classification consider either the non-consecutive
and long-distance semantics or the sequential semantics, but how to consider
them both coherently is less studied. In addition, most existing methods treat
output labels as independent methods, but ignore the hierarchical relations
among them, leading to useful semantic information loss. In this paper, we
propose a novel hierarchical taxonomy-aware and attentional graph capsule
recurrent CNNs framework for large-scale multi-label text classification.
Specifically, we first propose to model each document as a word order preserved
graph-of-words and normalize it as a corresponding words-matrix representation
which preserves both the non-consecutive, long-distance and local sequential
semantics. Then the words-matrix is input to the proposed attentional graph
capsule recurrent CNNs for more effectively learning the semantic features. To
leverage the hierarchical relations among the class labels, we propose a
hierarchical taxonomy embedding method to learn their representations, and
define a novel weighted margin loss by incorporating the label representation
similarity. Extensive evaluations on three datasets show that our model
significantly improves the performance of large-scale multi-label text
classification by comparing with state-of-the-art approaches.

我们提出了一种新的层级分类感知和注意图胶囊递归卷积神经网络框架，以实现大规模多标签文本分类。

面向大规模多标签文本分类的层次化分类感知及关注图胶囊 RCNNs

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for  Large-Scale Multi-Label Text Classification

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional
neural networks (CNNs) as discriminators within the generative adversarial
network (GAN) setting, while modeling image data. We provide guidelines for
designing CapsNet discriminators and the updated GAN objective function, which
incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
that CapsuleGAN outperforms convolutional-GAN at modeling image data
distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
adversarial metric and at semi-supervised image classification.

介绍了一种名为 CapsuleGAN 的生成对抗胶囊网络框架，使用胶囊网络作为辨别器，利用改进的 GAN 目标函数进行训练，可以比传统卷积神经网络更好地模拟图像数据分布，表现出色于 MNIST 和 CIFAR-10 数据集，并实现了半监督图像分类。