Results 21 to 30 of about 414,528 (301)
TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach
IEEE Access, 2018 Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data.
Lingyun Xiang +4 more
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IEEE Access, 2022 Unsupervised learning of feature representations is a challenging yet important problem for analyzing a large collection of multimedia data that do not have semantic labels.
Takahiko Furuya, Ryutarou Ohbuchi
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A Single-Stage Unsupervised Denoising Low-Illumination Enhancement Network Based on Swin-Transformer
IEEE Access, 2023 Traditional low-light enhancement methods are often based on paired datasets for training. The training data is difficult to obtain and the resulting model has poor generalization.
Qian Zhang +3 more
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Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders
Remote Sensing, 2019 This paper proposes novel autoencoders for unsupervised feature-learning from hyperspectral data. Hyperspectral data typically have many dimensions and a significant amount of variability such that many data points are required to represent the ...
Lloyd Windrim +4 more
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A comment on the training of unsupervised neural networks for learning phases
Results in Physics, 2022 The impact on the performance of an unsupervised neural network (NN) for learning the phases of two-dimensional ferromagnetic Potts model, namely a deep learning autoencoder (AE), from using various training sets is investigated.
Yuan-Heng Tseng, Fu-Jiun Jiang
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Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering
IEEE Access, 2023 Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers
Haoxiang Shi, Tetsuya Sakai
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Nuclear Engineering and Technology, 2022 This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing.
Younhee Choi +2 more
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Occlusion Aware Unsupervised Learning of Optical Flow
, 2018 It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods still has a relatively large gap compared to its supervised counterpart ...
Wang, Peng +5 more
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SUN: Stochastic UNsupervised Learning for Data Noise and Uncertainty Reduction
Applied SciencesUnsupervised learning methods significantly benefit various practical applications by effectively identifying intrinsic patterns within unlabelled data.
Nicholas Christakis, Dimitris Drikakis
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Unsupervised Learning of Edges
, 2016 Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object ...
Dollár, Piotr +3 more
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