This paper quantizes CapsNets for efficient edge deployment.
problem CapsNets require intense computations and are not suitable for resource-constrained edge devices.
method Developed a specialized quantization framework for CapsNets.
result Reduced memory footprint by 6.2x with only 0.15% accuracy loss.
A methodology for resilience analysis of Capsule Networks under approximation errors.
problem Resilience of Capsule Networks under approximation errors.
method Modeling and analyzing approximation errors in Capsule Networks' inference.
result Capsule Networks are more resilient to errors during dynamic routing than other stages.
PathCapsNet improves CapsNet by reducing parameters and enhancing performance.
problem Limitations of CapsNet, including excessive parameters and shallow architecture.
method Introducing a deep parallel multi-path version of CapsNet, incorporating depth, max-pooling, regularization, and new routing techniques.
result Better or comparable results to CapsNet with significantly reduced parameter count.
Deep CapsNet improves sign language recognition from wearable IMUs.
problem Continuous recognition of sign language from wearable devices.
method Custom CapsNet architecture using deep capsule networks and game theory.
result Improved accuracy of 94% and 92.50% for 3 and 5 routings respectively, compared to 87.99% for CNN.
Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificia…
New CapsNet and NLSTM framework improves traffic forecasting accuracy.
problem Accurate traffic forecasting for complex transportation networks.
method CapsNet for spatial features, nested LSTM for temporal dependencies.
result Framework outperforms multiple baseline models in Beijing network.
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…
Paper explores CapsNet for anomaly detection in images.
problem Detecting anomalies in unseen images using CapsNet.
method Developed prediction-probability and reconstruction-error based normality scores.
result CapsNet-based methods outperform benchmarks in anomaly detection.
Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract u…
FasTrCaps reduces CapsNet training time by 58.6% while maintaining accuracy.
problem The high training time of Capsule Networks (CapsNets).
method Integrating multiple lightweight optimizations and a novel learning rate policy (WarmAdaBatch) into FasTrCaps framework.
result FasTrCaps framework can reduce CapsNet training time by 58.6% while preserving accuracy.
Capsule Networks improve autonomous navigation in sparse environments.
problem Challenges in reinforcement learning for sparse reward environments.
method Caps-EM pairs CapsNets with Advantage Actor Critic, using fewer parameters.
result Caps-EM achieves significant time improvements and fewer parameters compared to competing methods.
Capsule networks detect and diagnose adversarial images better than CNNs.
problem Detecting and diagnosing adversarial images in neural networks.
method Class-conditional capsule reconstruction and reconstructive attack.
result Capsule networks outperform CNNs in detecting and diagnosing adversarial images.
NASCaps automates CapsNet design for better accuracy and hardware efficiency.
problem Designing Capsule Networks is laborious and inefficient.
method Automated Neural Architecture Search (NAS) with Genetic Algorithm optimization.
result Jointly optimizes network accuracy and hardware efficiency.
In this paper we propose novel Deformable Part Networks (DPNs) to learn {\em pose-invariant} representations for 2D object recognition. In contrast to the state-of-the-art pose-aware networks such as CapsNet \cite{sabour2017dynamic} and STN \cite{jaderberg2015spatial}, DPNs can be naturally {\em interpreted} as an effi…
A novel capsule network model improves surrogate modeling and uncertainty quantification from sparse data.
problem Surrogate modeling and uncertainty quantification of systems from sparse data.
method Adapted Capsule Network (CapsNet) architecture into image-to-image regression encoder-decoder network.
result The proposed approach accurately, efficiently, and robustly predicts responses for arbitrary diffusion fields.
Paper proposes a new model using Capsule Networks to classify malware types.
problem Classifying malware types with varying behaviors.
method Ensemble Capsule Network model using bootstrap aggregating.
result Improved accuracy in classifying malware types.
HitNet uses a Hit-or-Miss layer to enhance feature interpretability in capsule networks.
problem Difficulty in interpreting complex neural network architectures.
method Replacing the last layer with a Hit-or-Miss layer that trains capsules to hit or miss a target capsule using centripetal loss.
result HitNet achieves better performance than initial CapsNet on various datasets and provides interpretable feature representations.
Proposes a novel framework for multi-label text classification.
problem Lack of coherent consideration of non-consecutive and long-distance semantics and hierarchical relations among labels.
method Hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework.
result Significantly improves multi-label text classification performance.
Dual quality assessment method tackles adversarial robustness issues across various metrics.
problem Varying robustness levels and bias in adversarial attacks and defenses.
method Model agnostic dual quality assessment method, including robustness levels.
result Current networks and defenses are vulnerable at all robustness levels, highlighting the need for a dual approach.