A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …
literature. Classic clustering methods follow the assumption that data are represented as …
Image synthesis with adversarial networks: A comprehensive survey and case studies
Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …
various application domains such as computer vision, medicine, and natural language …
A style-based generator architecture for generative adversarial networks
We propose an alternative generator architecture for generative adversarial networks,
borrowing from style transfer literature. The new architecture leads to an automatically …
borrowing from style transfer literature. The new architecture leads to an automatically …
Unsupervised specific emitter identification method using radio-frequency fingerprint embedded InfoGAN
J Gong, X Xu, Y Lei - IEEE Transactions on Information …, 2020 - ieeexplore.ieee.org
Machine learning approaches are becoming increasingly popular to improve the efficiency
of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios …
of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios …
Verifying the union of manifolds hypothesis for image data
Deep learning has had tremendous success at learning low-dimensional representations of
high-dimensional data. This success would be impossible if there was no hidden low …
high-dimensional data. This success would be impossible if there was no hidden low …
Data augmentation in defect detection of sanitary ceramics in small and non-iid datasets
In this study, a data-augmentation method is proposed to narrow the significant difference
between the distribution of training and test sets when small sample sizes are concerned …
between the distribution of training and test sets when small sample sizes are concerned …
Disentangling latent space for vae by label relevant/irrelevant dimensions
Z Zheng, L Sun - Proceedings of the IEEE/CVF Conference …, 2019 - openaccess.thecvf.com
VAE requires the standard Gaussian distribution as a prior in the latent space. Since all
codes tend to follow the same prior, it often suffers the so-called" posterior collapse". To …
codes tend to follow the same prior, it often suffers the so-called" posterior collapse". To …
Precision-recall divergence optimization for generative modeling with GANs and normalizing flows
Achieving a balance between image quality (precision) and diversity (recall) is a significant
challenge in the domain of generative models. Current state-of-the-art models primarily rely …
challenge in the domain of generative models. Current state-of-the-art models primarily rely …
GEN: Generative equivariant networks for diverse image-to-image translation
Image-to-image (I2I) translation has become a key asset for generative adversarial
networks. Convolutional neural networks (CNNs), despite having a significant performance …
networks. Convolutional neural networks (CNNs), despite having a significant performance …
A generative adversarial network based framework for specific emitter characterization and identification
J Gong, X Xu, Y Qin, W Dong - 2019 11th International …, 2019 - ieeexplore.ieee.org
Specific emitter identification (SEI) enables the classification of various unique emitters
based on received waveforms using some external feature measurements from their …
based on received waveforms using some external feature measurements from their …