Using dynamic routing to extract intermediate features for developing scalable capsule networks

Mandal, Bodhisatwa; Ghosh, Swarnendu; Sarkhel, Ritesh; Das, Nibaran; Nasipuri, Mita

doi:10.1109/icaccp.2019.8883020

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The output of the capsules is forwarded to the decoder. The networks prevent overfitting by rebuilding the input image from the output capsules by minimizing the reconstruction loss as a regularization method in the decoder [ 43 ].…”

Section: Materials and Image Captioning Methodsmentioning

confidence: 99%

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Javanmardi

Latif

Sadeghi

et al. 2022

Sensors

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In image captioning models, the main challenge in describing an image is identifying all the objects by precisely considering the relationships between the objects and producing various captions. Over the past few years, many methods have been proposed, from an attribute-to-attribute comparison approach to handling issues related to semantics and their relationships. Despite the improvements, the existing techniques suffer from inadequate positional and geometrical attributes concepts. The reason is that most of the abovementioned approaches depend on Convolutional Neural Networks (CNNs) for object detection. CNN is notorious for failing to detect equivariance and rotational invariance in objects. Moreover, the pooling layers in CNNs cause valuable information to be lost. Inspired by the recent successful approaches, this paper introduces a novel framework for extracting meaningful descriptions based on a parallelized capsule network that describes the content of images through a high level of understanding of the semantic contents of an image. The main contribution of this paper is proposing a new method that not only overrides the limitations of CNNs but also generates descriptions with a wide variety of words by using Wikipedia. In our framework, capsules focus on the generation of meaningful descriptions with more detailed spatial and geometrical attributes for a given set of images by considering the position of the entities as well as their relationships. Qualitative experiments on the benchmark dataset MS-COCO show that our framework outperforms state-of-the-art image captioning models when describing the semantic content of the images.

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Section: Materials and Image Captioning Methodsmentioning

confidence: 99%

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Javanmardi

Latif

Sadeghi

et al. 2022

Sensors

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show abstract

“…The TL-CapsNet is a recent deep learning algorithm that functions better than conventional CNN in feature extraction and classification [52]. We improve a TL-CapsNet by designing a novel TL-CapsNet architecture, as our work requires processing the real and imaginary parts of a signal.…”

Section: Two-layer Capsule Network (Tl-capsnet)-based Amcmentioning

confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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“…Dynamic routing has an inner loop [28] [18] which contributes to hindering the algorithm to scale on complex data and increases the threat of overfitting when the network capacity is increased through an increase in the number of routing iterations. To test the models on this score, we varied the number of routing iterations and the results of these experiments are depicted in Fig.…”

Section: E Model's Ability To Scalementioning

confidence: 99%

Multi-lane LBP-Gabor Capsule Network with K-means Routing for Medical Image Analysis

Mensah¹,

Amponsah²,

Agyemang³

et al. 2021

IJACSA

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Using dynamic routing to extract intermediate features for developing scalable capsule networks

Cited by 7 publications

References 14 publications

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

Caps Captioning: A Modern Image Captioning Approach Based on Improved Capsule Network

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

Multi-lane LBP-Gabor Capsule Network with K-means Routing for Medical Image Analysis

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