New γ-capsule networks improve adversarial robustness and explainability of capsule networks.
problem Improving the robustness and explainability of capsule networks.
method Introducing γ-capsule networks with a new routing algorithm and training method. result Experimental results show γ-capsule networks are more robust and transparent. A method improves capsule networks by reducing information dilution and enhancing performance.
problem Capsule networks struggle with datasets containing background and complex objects.
method Restrict the activation values of primary capsule layers to highlight discriminative capsules.
result The method achieves better performance on various datasets.
Capsule networks can only represent symmetric functions due to routing limitations.
problem Capsule networks' expressivity is limited to symmetric functions.
method Proved and empirically demonstrated that EM-routing and routing-by-agreement prevent capsule networks from distinguishing inputs and their negative counterpart.
result Capsule networks are not universal approximators due to the limitation of expressivity.
Capsule networks improve on traditional neural networks by using vector activations.
problem Comparing capsule networks to traditional neural networks to validate their benefits.
method Deep visualization analysis, feature encoding across vector components, and instantiation parameter encoding.
result Capsule features encode information differently and provide benefits in computer vision applications.
Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
problem Vulnerability of capsule networks to adversarial attacks.
method Compared capsule networks to convolutional neural networks using various adversarial attacks.
result Capsule networks are vulnerable to adversarial attacks, similar to convolutional neural networks.
An unsupervised learning algorithm trains capsule networks for generating realistic images.
problem Training capsule networks for generating realistic images without labeled data.
method Developed an unsupervised learning algorithm using dynamic routing and an energy function for capsule networks.
result The algorithm successfully generates realistic looking images from a learned distribution.
Improved capsule networks with kernel methods for robustness.
problem Adversarial robustness of capsule networks.
method Kernelized capsule networks using Gaussian processes.
result Improved robustness to adversarial perturbations.
A new capsule routing algorithm derived from Variational Bayes improves performance in neural networks.
problem Improving performance of capsule networks in recognizing objects and their parts.
method Proposed a new capsule routing algorithm derived from Variational Bayes to fit a mixture of transforming gaussians.
result Significant improvement in MNIST to affNIST generalization over previous works.
Capsule networks improve with dynamic routing using Wasserstein objective.
problem Capsule networks struggle to consistently outperform traditional neural networks.
method Dynamic routing scheme using approximate Wasserstein objective to select capsules.
result Capsule network achieves over 1.2% improvement on CIFAR-10 with fewer parameters.
Capsule Networks improve clothing retrieval without landmark info.
problem In-shop clothing retrieval performance improvement.
method Triplet-based Capsule Network architecture with SC and RC blocks.
result Triplet Capsule Networks outperform FashionNet and SOTA architectures.
Capsule networks improve anomaly detection in high-dimensional datasets.
problem Anomaly detection in high-dimensional, class-imbalanced datasets.
method Used a capsule network architecture with autoencoder pre-training and dynamic routing.
result Capsule network outperformed other models in anomaly detection.
Capsule networks improve temporal data understanding, achieving 96.21% ECG accuracy.
problem Improving temporal data understanding with capsule networks.
method Generated capsules along temporal and channel dimensions, learning contrasting relationships.
result Achieved 96.21% accuracy on ECG signal beat categories, surpassing state-of-the-art.
CapsAttacks study imperceptible adversarial attacks on Capsule Networks, showing they can fool these networks.
problem Vulnerability of Capsule Networks to imperceptible adversarial attacks.
method Proposed a greedy algorithm for generating targeted imperceptible adversarial examples.
result Capsule Networks can be fooled by imperceptible adversarial attacks, similar to CNNs.
A new capsule network framework that preserves input transformations.
problem Inefficiency in learning part-whole relationships and lack of equivariance guarantees in capsule networks.
method Proposes a new capsule network framework that learns to projectively encode pose-variations for every capsule-type of each layer using a trainable, equivariant function over a grid of group-transformations.
result The proposed framework is equivariant and preserves the compositional representation of an input under transformations.
Capsules learn from expert neurons using dynamic routing.
problem Training complex multidimensional neural networks.
method Formulated capsule networks as a product of expert neurons with dynamic routing.
result Capsule networks can generate realistic images.
Project learns to model Capsule Networks' routing procedures for better expressiveness.
problem Limited expressiveness of Capsule Networks' inner routing procedures.
method Proposes two ways to learn the routing procedure as a network parameter.
result Improved expressiveness of Capsule Networks through learned routing procedures.
Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. …
Capsule networks improve audio classification performance.
problem Improving audio classification accuracy.
method Dynamic routing-by-agreement mechanism for capsule networks.
result Significant improvements in performance compared to baseline models.
Capsule networks improve performance on image classification tasks with fewer parameters.
problem Improving performance of capsule networks with fewer parameters.
method Inverted dot-product attention routing, Layer Normalization, concurrent iterative routing.
result Improves performance on benchmark datasets CIFAR-10 and CIFAR-100, and performs at-par with ResNet-18.
A capsule is a collection of neurons which represents different variants of a pattern in the network. The routing scheme ensures only certain capsules which resemble lower counterparts in the higher layer should be activated. However, the computational complexity becomes a bottleneck for scaling up to larger networks, …
Caps2NE learns node embeddings in graphs using capsule layers.
problem Learning low-dimensional node embeddings in graph data.
method Caps2NE uses a two-capsule layer architecture with a routing process to aggregate neighbors' features.
result Caps2NE achieves state-of-the-art performance on node classification tasks.
Automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukraine) was demonstrated by means of capsule deep learning neural network. It was applied to the image dataset of the carved Glagolitic and Cyrillic letters (CGCL), which was assembled and pr…
Researchers share pitfalls and improvements in implementing Hinton's capsule network.
problem Implementation pitfalls in Hinton's capsule network hindered progress in the field.
method Identifying and addressing common mistakes in capsule network implementations.
result Improved implementation of Hinton's capsule network outperforms existing open-source implementations.
Capsule networks improve at detecting changes in compositionality with routing.
problem Capsule networks struggle with detecting changes in compositionality.
method Introduced a loss function based on routing entropy to improve compositionality.
result Capsule networks with the new loss function better detect changes in compositionality.
Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image…
Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep lear…
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
problem Uninterpretable predictions from deep neural networks in healthcare.
method Teaches a novel multi-task capsule network to encode high-level visual attributes and malignancy scores.
result X-Caps outperforms state-of-the-art deep dense 3D CNNs in capturing visually interpretable attributes and malignancy prediction.
3D capsule network for point clouds handles rotations and translations.
problem Processing 3D point clouds with equivariance and invariance.
method Quaternion equivariant capsule network with dynamic routing on quaternions.
result Parsimonious capsule network disentangles geometry and pose.
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.
Stacked Capsule Autoencoders reconstruct objects from images using part relationships.
problem Reconstructing objects from images with robustness to viewpoint changes.
method Two-stage unsupervised capsule autoencoder that predicts part templates and object capsules.
result State-of-the-art results for unsupervised classification on SVHN and MNIST.
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackl…
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to …
Proposes a new neural network for better image segmentation.
problem Semantic segmentation of images.
method Capsule-based neural network with recursive traceback pipeline.
result Significantly enhances segmentation performance compared to FCN variants.
Capsule network improves polyp diagnosis accuracy.
problem Low accuracy of optical biopsy methods for polyps.
method D-Caps architecture with CAP method.
result 43% improvement over previous state-of-the-art.
Capsule models enforce object pose relationships for robustness, explored with probabilistic generative and variational methods.
problem Enforcing object pose relationships for robustness to viewpoint changes.
method Probabilistic generative model with variational bound, exploring capsule assumptions and inference mechanisms.
result Unified objective and test time optimisation demonstrated for capsule models.
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…
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.
With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue. We first formalize neural networks mathematically with their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, we set up a …
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.
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.
MKCapsnet improves schizophrenia identification using multi-kernels and dropout.
problem Identifying schizophrenia using existing methods requires two steps and large amounts of data.
method Developed a multi-kernel capsule network (MKCapsnet) inspired by brain anatomy.
result Outperformed state-of-the-art methods in schizophrenia identification.
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.
Improved Capsule Networks for better image restoration and context awareness.
problem Challenges in image context recognition for Capsule Networks.
method Expanding Pooling layers and increasing Reconstruction layers; comparing CNs with DL models on ASL dataset.
result Capsule Networks perform comparably to DL models but significantly reduce training time.
In recent years, convolutional neural networks (CNN) have played an important role in the field of deep learning. Variants of CNN's have proven to be very successful in classification tasks across different domains. However, there are two big drawbacks to CNN's: their failure to take into account of important spatial h…
Capsule Neural Networks classify graphs from categorical features and relationships.
problem Graph classification in scientific domains, especially with varying graph sizes and features.
method Explicit tensor representations, Capsule Network for classification.
result Capsule Network model performs competitively with state-of-the-art models.
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.
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in…
Proposes a method to balance imbalanced image datasets using capsule-GAN.
problem Imbalanced datasets challenge deep learning techniques.
method Capsule-GAN, combining GANs and capsule networks, addresses imbalance by generating minority class samples.
result Improves learning from imbalanced data with fewer parameters.