Results 41 to 50 of about 6,908 (119)
CoRR, 2023 Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces.
Aymene Mohammed Bouayed, David Naccache
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A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization
SN Applied Sciences, 2021 Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks.
R. Sekhar +3 more
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CoRR, 2019 Proceedings of the 37th International Conference on Machine ...
Michael Moor +3 more
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An Introduction to Autoencoders
CoRR, 2022 In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation ...
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Symmetric Wasserstein Autoencoders
CoRR, 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
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Detection of Pitting in Gears Using a Deep Sparse Autoencoder
Applied Sciences, 2017 In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network.
Yongzhi Qu +3 more
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Multiresolution convolutional autoencoders
Journal of Computational Physics, 2023 20 pages, 11 ...
Yuying Liu 0010 +3 more
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Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results
IEEE AccessThis paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives,
Azin Masoudi, Majid Ahmadi
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, 2020 Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning. In this work, we consider generative autoencoders based on maximum-mean discrepancy (MMD) and provide theoretical insights. In particular, (i) we prove that MMD coupled with Coulomb kernels has optimal convergence properties, which are similar to ...
Emanuele Sansone +2 more
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