SegCaps improves object segmentation accuracy with fewer parameters.
problem Object segmentation accuracy and efficiency.
method Convolutional-deconvolutional capsule network with locally-connected routing.
result SegCaps reduces parameter space by 95.4% while improving accuracy.
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.
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.
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.
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 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.
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.
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.
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 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 classify RAS protein structures with GPU acceleration.
problem Classifying RAS protein structures accurately and interpretably.
method Implemented Capsule Network architecture trained on 2D and 3D structural encodings.
result Capsule Network outperforms traditional CNNs in accuracy and interpretability.
Improved neural network efficiency through capsule routing and feedback mechanisms.
problem High computational complexity in capsule networks for larger networks.
method Approximated routing with two branches: master and aide, supervised and fast.
result Significant reduction in complexity and runtime with improved capsule agreement.
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.
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.
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.
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.
Capsule models detect adversarial images by reconstructing from top-level capsules.
problem Detecting adversarial images that look like a typical member of the predicted class.
method Capsule models trained to reconstruct images from pose parameters and identity of the correct top-level capsule.
result Setting a threshold on reconstruction error effectively detects adversarial images.
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.
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.
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.
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.
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 network improves handwriting recognition of historical graffiti.
problem Recognizing historical graffiti letters carved on stone walls.
method Used capsule deep neural network on a challenging dataset of 4000 images of 34 letters.
result Validation accuracy and AUC values were significantly higher for capsule network than for CNN.
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.
New Siamese Capsule Networks improve face verification in limited data settings.
problem Few-shot face verification with limited labeled data.
method Siamese Capsule Networks with contrastive loss and normalized pose features.
result Siamese Capsule Networks outperform baselines in few-shot face verification.
Capsule networks improve traffic sign detection accuracy.
problem Inability of CNNs to capture pose, view, orientation of traffic signs.
method Proposes capsule networks for traffic sign detection.
result Achieves 97.6% accuracy on GTSRB dataset.
CapsuleGAN uses capsule networks in GANs for better image data modeling.
problem Improving image data modeling in GANs.
method CapsuleGAN uses capsule networks as discriminators in GANs, with a new objective function incorporating margin loss.
result CapsuleGAN outperforms standard GANs on image data modeling and semi-supervised classification.
Unified framework for deep neural networks using capsule networks.
problem Unified description of deep neural networks.
method Formalized neural networks mathematically, set up a unified capsule framework.
result Unified framework simplifies existing deep neural networks and provides theoretical basis.
Geometric Capsule Autoencoders group 3D points into parts and objects.
problem Learning object representations from 3D point clouds.
method Geometric capsules with pose and feature components, Multi-View Agreement voting mechanism.
result Learned representations enable object identification and canonical pose recovery.
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.
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.
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.
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.
GCAPS-CNN improves GCNNs for graph classification.
problem Challenges in graph classification by current GCNN models.
method Integrates capsule idea into GCNNs for graph classification.
result Significantly outperforms state-of-the-art methods on benchmark datasets.
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
problem Time-consuming and high error rate in manual inspection of large numbers of similar capsule endoscopic images.
method Structural similarity analysis of visually salient areas and hierarchical clustering.
result 76% reduction in similar images, 100% lesion recall, 18-minute average play time.
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.
Paper proposes a capsule attention mechanism for EEG-EOG vigilance estimation.
problem Driver vigilance estimation for safer transportation.
method Capsule attention mechanism following LSTM network for multimodal EEG-EOG analysis.
result Capsule attention improves vigilance estimation robustness and accuracy.
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.
Proposes GANs using Capsule Networks for faster image synthesis.
problem Faster image synthesis with fewer training samples and epochs.
method Capsule Networks for image synthesis using GAN architectures.
result Learn data manifold faster and synthesize visually accurate images.
Capsule networks improve classification accuracy on complex data.
problem CNN's failure to account for spatial hierarchies and lack of rotational invariance.
method Dynamic routing and reconstruction regularization to create capsules that are both rotation invariant and spatially aware.
result Capsule networks achieve a state-of-the-art 0.25% test error on MNIST without data augmentation.
A novel Gaussian process approach for deconvolution of missing data signals.
problem Recovering a latent source from observations with missing parts and unknown filters.
method Gaussian process prior for Bayesian nonparametric deconvolution.
result The proposed Gaussian process deconvolution (GPDC) method is feasible and effective.
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 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.
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem.…
New algorithm robustly solves blind deconvolution problems.
problem Robustly solving blind deconvolution problems in the presence of noise and perturbations.
method Mirror Descent algorithm for robust continuous optimization.
result Provable robustness and convergence guarantees for the algorithm.
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.
problem Challenges in extracting meaningful peaks from noisy or complex spectra.
method Bayesian spectral deconvolution coupled with a physical-property regression layer.
result Recovery of weak peaks in poly(lactic acid) IR spectra related to degradation rates.