NTIRE 2024 challenge on low light image enhancement: Methods and results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting
the proposed solutions and results. The aim of this challenge is to discover an effective …
the proposed solutions and results. The aim of this challenge is to discover an effective …
Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks
V Kunc, J Kléma - arXiv preprint arXiv:2402.09092, 2024 - arxiv.org
Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …
many areas of life. Recently, their importance and practical usability have further been …
Explaining time series via contrastive and locally sparse perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying
important locations in the time series and matching complex temporal patterns. Although …
important locations in the time series and matching complex temporal patterns. Although …
Deep continuous convolutional networks for fault diagnosis
Convolutional neural network (CNN) architectures have been extensively utilized in data-
driven fault diagnosis and have demonstrated significant success. However, there remain …
driven fault diagnosis and have demonstrated significant success. However, there remain …
Iieu: Rethinking neural feature activation from decision-making
S Cai - Proceedings of the IEEE/CVF International …, 2023 - openaccess.thecvf.com
Abstract Nonlinear Activation (Act) models which help fit the underlying mappings are critical
for neural representation learning. Neuronal behaviors inspire basic Act functions, eg …
for neural representation learning. Neuronal behaviors inspire basic Act functions, eg …
AdaShift: Learning Discriminative Self-Gated Neural Feature Activation With an Adaptive Shift Factor
S Cai - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Nonlinearities are decisive in neural representation learning. Traditional Activation (Act)
functions impose fixed inductive biases on neural networks with oriented biological …
functions impose fixed inductive biases on neural networks with oriented biological …
Three-dimensional hybrid fusion networks for current-based bearing fault diagnosis
Intelligent fault diagnosis (IFD) techniques commonly use vibration-based measurements to
perform health monitoring of critical rotating components in industrial systems. However …
perform health monitoring of critical rotating components in industrial systems. However …
Bayesian Optimization via Exact Penalty
J Zhao, J Xu - Technometrics, 2024 - Taylor & Francis
Constrained optimization problems pose challenges when the objective function and
constraints are nonconvex and their evaluation requires expensive black-box simulations …
constraints are nonconvex and their evaluation requires expensive black-box simulations …
Sclmnet: A dual-branch guided network for lung and lung lobe segmentation
S Zhang, H Yuan, H Cao, M Yang, C Zhang - Biomedical Signal Processing …, 2023 - Elsevier
Lung and lung lobe segmentation are two crucial techniques for lung imaging analysis that
interact in clinical settings. Lung segmentation assists physicians in comparing different …
interact in clinical settings. Lung segmentation assists physicians in comparing different …
Set-Valued Regression of Wind Power Curve
X Shen - IEEE Transactions on Sustainable Energy, 2024 - ieeexplore.ieee.org
Precise wind power curves are pivotal for monitoring the status of wind turbines and
predicting wind power, which are important parts of utilizing wind energy in power systems …
predicting wind power, which are important parts of utilizing wind energy in power systems …