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Hyperparameter optimisation for Capsule Networks
2018
EAI Endorsed Transactions on Cloud Systems
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Hence, Hinton et al, proposed a layered architecture called Capsule Networks (Capsnets) which outperform traditional systems by replacing pooling techniques with dynamic routing abilities. ...
Convolutional Neural Networks and its contemporary variants have proven to be ruling benchmarks for most image processing tasks but resort to pooling techniques and routing mechanisms that affect classification ...
2 EAI Endorsed Transactions on Cloud Systems Online First Hyperparameter optimisation for Capsule Networks 3 deals with the experimentation framework with proposed changes followed by results obtained ...
doi:10.4108/eai.13-7-2018.158416
fatcat:6yzmnhvffff53hmcgn32eieyem
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data
[article]
2022
arXiv
pre-print
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Experimental results show that without hyperparameter optimisation, using Capsules significantly reduces overfitting and improves the training efficiency. ...
This paper introduces a novel NN architecture which hybridises the Long-Short-Term-Memory (LSTM) and Capsule Networks into a single network in a branched input Autoencoder architecture for use on multivariate ...
Most importantly, the results in Table III for training using non-optimised hyperparameters suggest that with the use of Capsules, the hyperparameter optimisation procedure can be simplified considerably ...
arXiv:2202.05538v1
fatcat:ojqo47ktdbgptlufnx4caa3bji
Capsule Networks – A Probabilistic Perspective
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.03553v2 fatcat:hyv2vrycdrb5lhcnumvadg2y3iOther Versions
We experimentally demonstrate the applicability of our unified objective, and demonstrate the use of test time optimisation to solve problems inherent to amortised inference in our model. ...
'Capsule' models try to explicitly represent the poses of objects, enforcing a linear relationship between an object's pose and that of its constituent parts. ...
Kosiorek for making their code avaliable, and for helpful discussions. We would also like to thank all the members of OATML & OXCSML for support and feedback. ...
arXiv:2004.03553v3
fatcat:6ae7g4ylyrfgxlf6xapaqqfphi
Automatic Segmentation of Prostate MRI using Convolutional Neural Networks: Investigating the Impact of Network Architecture on the Accuracy of Volume Measurement and MRI-Ultrasound Registration
2019
Medical Image Analysis
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For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing ...
Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now ...
Acknowledgements We would like to acknowledge the UCL EPSRC Centre for Doctoral Training in Medical Imaging (Grant No. EP/L016478/1 ) for supporting Nooshin Ghavami in this work. ...
doi:10.1016/j.media.2019.101558
pmid:31526965
pmcid:PMC7985677
fatcat:g2qao37o3zehrakoqepobgyy5a
Capsule Routing via Variational Bayes
[article]
2019
arXiv
pre-print
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In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE ...
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. ...
Figure 3 : 3 Direct comparison between VB and EM † routing validation set error using identical networks and hyperparameters. ...
arXiv:1905.11455v3
fatcat:fz7lck4gpvchtjwq25ktuw3uoq
Capsule Routing via Variational Bayes
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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In this paper, we propose a new capsule routing algorithm derived from Variational Bayes for fitting a mixture of transforming gaussians, and show it is possible transform our capsule network into a Capsule-VAE ...
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks. ...
Figure 3 : 3 Direct comparison between VB and EM † routing validation set error using identical networks and hyperparameters. ...
doi:10.1609/aaai.v34i04.5785
fatcat:ztz7sihu7nc7dpmwawxwb4kuti
Ordinal Pooling Networks: For Preserving Information over Shrinking Feature Maps
[article]
2018
arXiv
pre-print
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To address this issue, a novel pooling scheme, Ordinal Pooling Network (OPN), is introduced in this work. ...
rearranges all the elements of a pooling region in a sequence and assigns different weights to these elements based upon their orders in the sequence, where the weights are learned via the gradient-based optimisation ...
CapsNet consists of layers of capsules, where each capsule represents a group of neurons. ...
arXiv:1804.02702v2
fatcat:73sxn3f33vgmvauhmydzcladcu
Multi‐scale capsule generative adversarial network for snow removal
2021
IET Computer Vision
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In this work, we propose an effective multi-scale generative adversarial network framework for single-image snow removal, which is built with a multi-scale structure to identify various scales of snowflakes ...
Snowflakes captured on photos may severely decrease the visual quality and cause difficulties for vision analysis systems. ...
The hyperparameters of guided filter in [31] may suits low transparency snowflakes, but failed for opaque ones. ...
doi:10.1049/cvi2.12038
fatcat:ohkaljuclfh5xklqtvn4s4isba
SecProCT: In Silico Prediction of Human Secretory Proteins Based on Capsule Network and Transformer
2021
International Journal of Molecular Sciences
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The main contributions of this article are as follows: (1) a deep learning model based on a capsule network and transformer architecture is proposed for predicting secretory proteins. ...
In this article, we propose a deep learning model based on the capsule network and transformer architecture, SecProCT, to predict secretory proteins using only amino acid sequences. ...
The data input representation of the network directly determines the effectiveness of a series of settings, such as the network structure, loss function, and hyperparameters, and determines the upper limit ...
doi:10.3390/ijms22169054
pmid:34445760
pmcid:PMC8396571
fatcat:b5ycisfakfbftcj7ugtzjpavlm
Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning
2021
Materials
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These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and ...
Acknowledgments: The authors are sincerely grateful to European Commission for the financial sponsorship of the H2020-RISE Project No. 691135 "RISEN: Rail Infrastructure Systems Engineering Network," which ...
enables a global research network that tackles the grand challenge in railway infrastructure resilience and advanced sensing in extreme environments (www.risen2rail.eu, accessed on ...
doi:10.3390/ma14154068
fatcat:sckh24xauvbr7koyuebvtwlrmy
Learning Temporal Clusters Using Capsule Routing for Speech Emotion Recognition
2019
Interspeech 2019
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In this paper, we propose a novel temporal modelling framework for robust emotion classification using bidirectional long short-term memory network (BLSTM), CNN and Capsule networks. ...
For FAO-Aibo and RAVDESS 77.6% and 56.2% accuracy was achieved, respectively, which is 3% and 14% (absolute) higher than the best-reported result for the respective tasks. ...
Networks were trained by PyTorch [30] and optimisation was done by Adam [31] . ...
doi:10.21437/interspeech.2019-3068
dblp:conf/interspeech/JalalLMH19
fatcat:xmuwppf2ivccjjd3rce6z5mb7i
Learning Compositional Structures for Deep Learning: Why Routing-by-agreement is Necessary
[article]
2020
arXiv
pre-print
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Thus, we introduce the entropy of routing weights as a loss function for better compositionality among capsules. ...
We present a formal grammar description of convolutional neural networks and capsule networks that shows how capsule networks can enforce such parse-tree structures, while CNNs do not. ...
We did not aim for state-of-the-art results which may be possible with explicit hyperparameter tuning. ...
arXiv:2010.01488v2
fatcat:erosmzn5rffedmxbrbsb2chjwy
Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture
2020
Scientific Reports
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We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. ...
Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. ...
Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. Figures 2 and 3 were drawn by the authors. ...
doi:10.1038/s41598-020-74668-8
pmid:33077755
fatcat:5ef6miiuwbgmdjfpyw3nm4jugu
A micro-XRT image analysis and machine learning methodology for the characterisation of multi-particulate capsule formulations
2020
International Journal of Pharmaceutics: X
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capsule. ...
The application of X-ray microtomography for quantitative structural analysis of pharmaceutical multi-particulate systems was demonstrated for commercial capsules, each containing approximately 300 formulated ...
The TC-SVM models' hyperparameter optimisation yielded changing values for the applied box constraints (BoxC) of both classes with high regularization for the majority non-broken pellet class (BoxC = 0.29 ...
doi:10.1016/j.ijpx.2020.100041
pmid:32025658
pmcid:PMC6997304
fatcat:kzgw5jo44zff3gcx6ttnl43tzu
Weakly-supervised convolutional neural networks for multimodal image registration
2018
Medical Image Analysis
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only unlabelled image pairs are used as the network input for inference. ...
Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. ...
The authors would like to thank colleagues Weidi Xie from Oxford and Carole Sudre from UCL for helpful discussions, and Rachael Rodell from SmartTarget Ltd. for the assistance in data collection. ...
doi:10.1016/j.media.2018.07.002
pmid:30007253
fatcat:hnovrfpd55b3pmu3kpnrzqp27q
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