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Spatially regularized active diffusion learning for high-dimensional images
An active learning algorithm for the classification of high-dimensional ...
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Active Deep Learning for Classification of Hyperspectral Images
Active deep learning classification of hyperspectral images is considere...
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Learning by Active Nonlinear Diffusion
This article proposes an active learning method for high dimensional dat...
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Diffusion-based Deep Active Learning
The remarkable performance of deep neural networks depends on the availa...
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Connecting exciton diffusion with surface roughness via deep learning
Exciton diffusion plays a vital role in the function of many organic sem...
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Exploring the high dimensional geometry of HSI features
We explore feature space geometries induced by the 3-D Fourier scatterin...
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Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering
An unsupervised learning algorithm to cluster hyperspectral image (HSI) ...
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Deep Diffusion Processes for Active Learning of Hyperspectral Images
A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs. A deep variational autoencoder extracts smoothed, denoised features from a high-dimensional HSI, which are then used to make labeling queries based on graph diffusion processes. The proposed method combines the robust representations of deep learning with the mathematical tractability of diffusion geometry, and leads to strong performance on real HSI.
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