Results 151 to 160 of about 75,818 (178)
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Hyperspectral Anomaly Detection With Guided Autoencoder
IEEE Transactions on Geoscience and Remote Sensing, 2022Recently, autoencoder (AE)-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The AE can simultaneously reconstruct both the anomaly targets and the background, but the lack of prior ...
Pei Xiang +3 more
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Geometry Regularized Autoencoders
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning.
Andres F. Duque +3 more
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Autoencoder in Autoencoder Networks
IEEE Transactions on Neural Networks and Learning SystemsModeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets).
Changqing Zhang +5 more
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Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
International Conference on Learning RepresentationsWe present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x ...
Junyu Chen +8 more
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A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery
, 2021Accurate fault diagnosis is critical to the safe and reliable operation of rotating machinery. Intelligent fault diagnosis techniques based on deep learning have recently gained increasing attention due to their ability to rapidly and efficiently extract
Xinya Wu +3 more
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Autoencoder asset pricing models
Journal of Econometrics, 2020We propose a new latent factor conditional asset pricing model. Like Kelly, Pruitt, and Su (KPS, 2019), our model allows for latent factors and factor exposures that depend on covariates such as asset characteristics. But, unlike the linearity assumption
Shihao Gu, Bryan T. Kelly, D. Xiu
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One2Multi Graph Autoencoder for Multi-view Graph Clustering
The Web Conference, 2020Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years.
Shaohua Fan +5 more
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AE2-Nets: Autoencoder in Autoencoder Networks
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019Learning on data represented with multiple views (e.g., multiple types of descriptors or modalities) is a rapidly growing direction in machine learning and computer vision. Although effectiveness achieved, most existing algorithms usually focus on classification or clustering tasks.
Changqing Zhang, Yeqing Liu, Huazhu Fu
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Variational Autoencoders Versus Denoising Autoencoders for Recommendations
2021Recommender systems help users explore new content such as music and news by showing them what they will find potentially interesting. There are many methods and algorithms that can help recommender systems create personalized recommendations.
Khadija Bennouna +4 more
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A comprehensive survey on design and application of autoencoder in deep learning
Applied Soft Computing, 2023Pengzhi Li, Yan Pei, Jianqiang Li
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