CapsuleGAN: Generative Adversarial Capsule Network

Jaiswal, Ayush; AbdAlmageed, Wael; Wu, Yue; Natarajan, Premkumar

doi:10.1007/978-3-030-11015-4_38

Cited by 128 publications

(77 citation statements)

References 9 publications

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“…Dilin et al [23] solves the dynamic routing as an optimization problem, and achieves better performance by introducing KL divergence between the coupling distribution. CapsGan [11] uses a capsule network as the discriminator in the GAN pipeline, to get visually better results than convolutional GANs. In contrast to these, our work focuses on going deeper with the capsule networks and increase its performance on more complex datasets.…”

Section: Related Workmentioning

confidence: 99%

DeepCaps: Going Deeper With Capsule Networks

Rajasegaran

Jayasundara

Jayasekara

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

190

175

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Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps 1 , a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

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Section: Related Workmentioning

confidence: 99%

DeepCaps: Going Deeper With Capsule Networks

Rajasegaran

Jayasundara

Jayasekara

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

190

175

View full text Add to dashboard Cite

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“…In another domain, Iesmantas and Alzbutas applied a capsule network based on binary classification to breast cancer detection [69]. Jaiswal et al reported a capsule-based GAN [70]. Yang et al applied a capsule network to the text domain [71].…”

Section: Capsule Networkmentioning

confidence: 99%

Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

Nguyen

Yamagishi

Echizen

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

504

224

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The revolution in computer hardware, especially in graphics processing units and tensor processing units, has enabled significant advances in computer graphics and artificial intelligence algorithms. In addition to their many beneficial applications in daily life and business, computergenerated/manipulated images and videos can be used for malicious purposes that violate security systems, privacy, and social trust. The deepfake phenomenon and its variations enable a normal user to use his or her personal computer to easily create fake videos of anybody from a short real online video. Several countermeasures have been introduced to deal with attacks using such videos. However, most of them are targeted at certain domains and are ineffective when applied to other domains or new attacks. In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning. It uses many fewer parameters than traditional convolutional neural networks with similar performance. Moreover, we explain, for the first time ever in the literature, the theory behind the application of capsule networks to the forensics problem through detailed analysis and visualization.

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“…ations than a typical CNN thanks to using a vector output instead of a scalar one, and by replacing the max-pooling operation with a dynamic routing algorithm. Although Capsules have some advantages over CNNs, as demonstrated by the encouraging results on 2D image classification [18,7] and segmentation [10], there is currently no work proposed based on the Capsule Network for 3D object classification. Therefore, in this paper, we propose an extension of Capsule Networks such that it becomes applicable to 3D point cloud data, and investigate the utility for the purpose of 3D object classification.…”

Section: Related Workmentioning

confidence: 99%

3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds

Cheraghian

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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This paper introduces the 3DCapsule, which is a 3D extension of the recently introduced Capsule concept that makes it applicable to unordered point sets. The original Capsule relies on the existence of a spatial relationship between the elements in the feature map it is presented with, whereas in point permutation invariant formulations of 3D point set classification methods, such relationships are typically lost. Here, a new layer called ComposeCaps is introduced that, in lieu of a spatially relevant feature mapping, learns a new mapping that can be exploited by the 3DCapsule. Previous works in the 3D point set classification domain have focused on other parts of the architecture, whereas instead, the 3DCapsule is a drop-in replacement of the commonly used fully connected classifier. It is demonstrated via an ablation study, that when the 3DCapsule is applied to recent 3D point set classification architectures, it consistently shows an improvement, in particular when subjected to noisy data. Similarly, the ComposeCaps layer is evaluated and demonstrates an improvement over the baseline. In an apples-to-apples comparison against state-ofthe-art methods, again, better performance is demonstrated by the 3DCapsule.

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CapsuleGAN: Generative Adversarial Capsule Network

Cited by 128 publications

References 9 publications

DeepCaps: Going Deeper With Capsule Networks

DeepCaps: Going Deeper With Capsule Networks

Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos

3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds

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