Results 61 to 70 of about 83,749 (307)
LLNet: A deep autoencoder approach to natural low-light image enhancement [PDF]
Pattern Recognition, 2015 In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission.
Kin Gwn Lore +2 more
semanticscholar +1 more source
, 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
openaire +2 more sources
Symmetric Wasserstein Autoencoders
CoRR, 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
openaire +4 more sources
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization [PDF]
IEEE International Conference on Computer Vision, 2017 In this paper, we propose a new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments.
Kamran Ghasedi Dizaji +4 more
semanticscholar +1 more source
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
Bioengineering, 2023 Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications.
Fatemeh Esmaeili +5 more
semanticscholar +1 more source
Advanced Engineering Materials, EarlyView.A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed +6 more
wiley +1 more source
The deep kernelized autoencoder [PDF]
Applied Soft Computing, 2018 Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of the input data need to be captured by the codes. Kernel machines have experienced great success by operating via inner-products in a theoretically well-defined reproducing ...
Michael Kampffmeyer +4 more
openaire +4 more sources
An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder
AerospaceThe paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It
Shenghan Zhou +3 more
semanticscholar +1 more source
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction [PDF]
IEEE International Conference on Computer Vision, 2017 In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with
A. Tewari +6 more
semanticscholar +1 more source