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Deep RGB-Driven Learning Network for Unsupervised Hyperspectral Image Super-Resolution
Hyperspectral (HS) images are used in many fields to improve the analysis and understanding performance of captured scenes, as they contain a wide...
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Deep Learning Overview
Among the various machine learning algorithms, deep learning has recently been dramatically used in different scopes. Deep learning models have been...
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Research trends in deep learning and machine learning for cloud computing security
Deep learning and machine learning show effectiveness in identifying and addressing cloud security threats. Despite the large number of articles...
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An unsupervised statistical representation learning method for human activity recognition
With the evolution of smart devices like smartphones, smartwatches, and other wearable devices, motion sensors have been integrated into these...
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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning
This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance...
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Unsupervised learning based dual-branch fusion low-light image enhancement
Distortion-free enhancement on images captured under low-light conditions has always been a challenging problem in computer vision. Although many...
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Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-identification
With rapid advancements in depth sensors and deep learning, skeleton-based person re-identification (re-ID) models have recently achieved remarkable...
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Learning cross-domain representations by vision transformer for unsupervised domain adaptation
Unsupervised Domain Adaptation (UDA) is a popular machine learning technique to reduce the distribution discrepancy among domains. Generally, most...
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Machine Learning and Deep Learning
Machine Learning is a sub-category of Artificial Intelligence enabling computers with the ability of pattern recognition, or to continuously learn...
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Geometric Deep Learning for Unsupervised Registration of Diffusion Magnetic Resonance Images
Deep learning based models for registration predict a transformation directly from moving and fixed image appearances. These models have...
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SpFusionNet: deep learning-driven brain image fusion with spatial frequency analysis
In the domain of multi-focus (MF) and multi-model image fusion (MMIF), accurately merging focused regions from various images remains a challenge....
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Wavelet-based spectrum transfer with collaborative learning for unsupervised bidirectional cross-modality domain adaptation on medical image segmentation
Unsupervised cross-modality domain adaptation for medical image segmentation has made great progress with the development of adversarial...
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A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties
Rice is an essential staple food for human nutrition. Rice varieties worldwide have been planted, imported, and exported. During production and...
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Contrast-based unsupervised hashing method with margin limit
The unsupervised hash image retrieval method based on contrastive learning has been widely recognized and concerned because it can make better use of...
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Exploring Enhanced Recognition in Gesture Language Videos Through Unsupervised Learning of Deep Autoencoder
The primary objective of a neural network is to achieve generalization, enabling it to recognize previously unseen data from the same category. This...
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Motion Compensated Unsupervised Deep Learning for 5D MRI
We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions....
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When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is typically a slow process because of its sequential data acquisition. To speed up this process, MR acquisition is...
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Noise4Denoise: Leveraging noise for unsupervised point cloud denoising
Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth...
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Unsupervised skeleton-based action representation learning via relation consistency pursuit
In this paper, we propose a Skeleton-based Relation Consistency Learning scheme (SRCL) for unsupervised 3D action representation learning. By...
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Unsupervised learning of probabilistic subspaces for multi-spectral and multi-temporal image-based disaster map**
Accurate and timely identification of regions damaged by a natural disaster is critical for assessing the damages and reducing the human life cost....
