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
doaj +1 more source
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
doaj +1 more source
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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Statistical physics of unsupervised learning with prior knowledge in neural networks
, 2020 Integrating sensory inputs with prior beliefs from past experiences in unsupervised learning is a common and fundamental characteristic of brain or artificial neural computation.
Hou, Tianqi, Huang, Haiping
core +1 more source
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
doaj +1 more source
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation [PDF]
Computer Vision and Pattern Recognition, 2018 We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Anurag Ranjan +6 more
semanticscholar +1 more source
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
doaj +1 more source
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
IEEE Access, 2019 While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning.
Muhammad Usama +7 more
doaj +1 more source
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
doaj +1 more source
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
core +1 more source