DualGAN: Unsupervised Dual Learning for Image-to-Image Translation [PDF]
IEEE International Conference on Computer Vision, 2017 Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently [7, 8, 21, 12, 4, 18]. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a ...
Zili Yi, Hao Zhang, P. Tan, Minglun Gong
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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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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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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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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
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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 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
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