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Hyperparameter optimisation for Capsule Networks

Gagana B, S Natarajan
2018 EAI Endorsed Transactions on Cloud Systems  
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]

Ayman Elhalwagy, Tatiana Kalganova
2022 arXiv   pre-print
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]

Lewis Smith and Lisa Schut and Yarin Gal and Mark van der Wilk
2021 arXiv   pre-print
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

Nooshin Ghavami, Yipeng Hu, Eli Gibson, Ester Bonmati, Mark Emberton, Caroline M. Moore, Dean C. Barratt
2019 Medical Image Analysis  
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]

Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
2019 arXiv   pre-print
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

Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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]

Ashwani Kumar
2018 arXiv   pre-print
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

Fei Yang, Jialu Zhang, Qian Zhang
2021 IET Computer Vision  
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

Wei Du, Xuan Zhao, Yu Sun, Lei Zheng, Ying Li, Yu Zhang
2021 International Journal of Molecular Sciences  
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

Xu Huang, Mirna Wasouf, Jessada Sresakoolchai, Sakdirat Kaewunruen
2021 Materials  
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

Md. Asif Jalal, Erfan Loweimi, Roger K. Moore, Thomas Hain
2019 Interspeech 2019  
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]

Sai Raam Venkatraman, Ankit Anand, S. Balasubramanian, R. Raghunatha Sarma
2020 arXiv   pre-print
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

Reinier Noorda, Andrea Nevárez, Adrián Colomer, Vicente Pons Beltrán, Valery Naranjo
2020 Scientific Reports  
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

Frederik J.S. Doerr, Alastair J. Florence
2020 International Journal of Pharmaceutics: X  
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

Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble (+2 others)
2018 Medical Image Analysis  
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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