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IEEE Transactions on Geoscience and Remote Sensing, 2021
—Class-wise adversarial adaptation networks are investigated for the classification of hyperspectral remote sensing images in this article. By adversarial learning between the feature extractor and the multiple domain discrimi-nators, domain-invariant ...
Zixu Liu, Li Ma, Q. Du
semanticscholar +1 more source
—Class-wise adversarial adaptation networks are investigated for the classification of hyperspectral remote sensing images in this article. By adversarial learning between the feature extractor and the multiple domain discrimi-nators, domain-invariant ...
Zixu Liu, Li Ma, Q. Du
semanticscholar +1 more source
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
IEEE International Conference on Computer Vision, 2023This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference.
R. Abdelfattah +4 more
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Fully memristive neural networks for pattern classification with unsupervised learning
Nature Electronics, 2018Zhongrui Wang, Saumil Joshi, Wenhao Song
exaly +2 more sources
IEEE Transactions on Geoscience and Remote Sensing, 2022
In recent years, deep-learning-based hyperspectral image (HSI) classification methods have achieved significant development. The superior capability of feature extraction from these data-driven methods dramatically improves classification performance ...
Yifan Sun +5 more
semanticscholar +1 more source
In recent years, deep-learning-based hyperspectral image (HSI) classification methods have achieved significant development. The superior capability of feature extraction from these data-driven methods dramatically improves classification performance ...
Yifan Sun +5 more
semanticscholar +1 more source
IEEE Transactions on Cybernetics, 2020
Due to high dimensionality and multiple variables, unsupervised classification of multivariate time series (MTS) involves more challenging problems than those of univariate ones.
Hong He, Yonghong Tan
semanticscholar +1 more source
Due to high dimensionality and multiple variables, unsupervised classification of multivariate time series (MTS) involves more challenging problems than those of univariate ones.
Hong He, Yonghong Tan
semanticscholar +1 more source
Unsupervised transfer classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010We study the problem of building the classification model for a target class in the absence of any labeled training example for that class. To address this difficult learning problem, we extend the idea of transfer learning by assuming that the following side information is available: (i) a collection of labeled examples belonging to other classes in ...
Tianbao Yang +4 more
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