GLL: A Differentiable Graph Learning Layer for Neural Networks
The graph learning layer (GLL) replaces the usual projection head and softmax classifier with graph-Laplacian label propagation. Samples in a minibatch become nodes of a similarity graph, allowing predictions to use relationships among examples instead of classifying each representation independently.
Dec 11, 2024
Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing
This work formulates nearly blind hyperspectral unmixing as a semi-supervised problem: instead of assuming all endmember spectra are known, it requests abundance information or simple one-hot pseudo-labels for only a very small set of pixels selected by graph-based active learning.
Sep 11, 2023