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Unsupervised Domain Adaptation Techniques
This chapter provides an overview of unsupervised domain adaptation techniques. First, we identify key challenges and limitations in current...
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Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study
The severe progression of Diabetes Mellitus (DM) stands out as one of the most significant concerns for healthcare officials worldwide. Diabetic...
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EML for Unsupervised Learning
This chapter introduces the use of Evolutionary Machine Learning (EML) techniques for unsupervised machine learning tasks. First, a brief...
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Analysis of cloud computing-based education platforms using unsupervised random forest
Cloud computing-based online education has played a vital role in enabling uninterrupted learning during crises such as the COVID-19 pandemic. This...
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Lifelong learning gets better with MixUp and unsupervised continual representation
Continual learning enables learning systems to adapt to evolving data distributions by sequentially acquiring knowledge from a series of tasks....
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Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) is a well-explored domain in transfer learning, finding applications across various real-world scenarios. The...
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Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering
Data augmentation has contributed to the rapid advancement of unsupervised learning on 3D point clouds. However, we argue that data augmentation is...
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A hybrid model for unsupervised single channel speech separation
The performance of any voice recognition platform in real environment depends on how well the desired speech signal is separated from unwanted...
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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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Assessment of energy-efficient wireless network using autoencoders with unsupervised deep learning
The propagation of wireless networks in e-business applications demands efficient and robust anomaly detection techniques to ensure data security and...
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Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model
Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by...
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Predicting document novelty: an unsupervised learning approach
In the age of information deluge, it is pivotal to have access to information or knowledge which is not just relevant but also, novel. Knowledge...
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Unsupervised Classification
In this fifth chapter, we are going to see the theoretical foundations of Unsupervised Classification of events and the main techniques used to carry...
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Improving unsupervised domain adaptation through class-conditional compact representations
A major technique for tackling unsupervised domain adaptation involves map** data points from both the source and target domains into a shared...
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Unsupervised diffusion based anomaly detection for time series
Unsupervised anomaly detection aims to construct a model that effectively detects invisible anomalies by training and reconstruct normal data. While...
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Unsupervised spectral feature selection algorithms for high dimensional data
It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data, especially...
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Artistic image synthesis from unsupervised segmentation maps
We present a framework for artwork image synthesis from unsupervised segmentation maps input and style images. The output has style consistency with...
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Loose–tight cluster regularization for unsupervised person re-identification
Unsupervised person re-identification (Re-ID) is a critical and challenging task in computer vision. It aims to identify the same person across...
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Unsupervised contrastive learning with simple transformation for 3D point cloud data
Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for...
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Unsupervised clustering for intrinsic mode functions selection in Hyperspectral image classification
In the realm of hyperspectral image classification, traditional methods typically eliminate spectrum noise to enhance spectral features, followed by...
