GLL: A Differentiable Graph Learning Layer for Neural Networks

Wed, 11 Dec 2024 00:00:00 +0000

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.

The main technical contribution is an exact end-to-end differentiation rule for a broad family of graph learning methods. By deriving the backward pass with the adjoint method, the graph solver can participate directly in neural-network training rather than being detached from the feature extractor or differentiated through a heuristic approximation.

Experiments show smoother transitions between classes, improved optimization and generalization, and stronger resistance to adversarial perturbations than a conventional softmax head. The framework supports both supervised and semi-supervised learning and is released with an open-source implementation.

Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing

Mon, 11 Sep 2023 00:00:00 +0000

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.

Two complementary models are introduced. Graph Learning Unmixing (GLU) interprets graph Laplace class probabilities directly as abundance maps and then estimates the endmembers. Graph-Regularized Semi-supervised Unmixing (GRSU) combines the linear mixing model, nonnegativity and sum-to-one constraints, graph regularization, and supervision on the queried pixels in a joint optimization problem initialized by GLU.

Experiments on Urban, Samson, and Jasper Ridge imagery show that both exact abundances and easy-to-obtain one-hot labels substantially improve blind-unmixing baselines. With only 0.4% of pixels labeled, the proposed methods improve state-of-the-art blind approaches by 50% or more on the reported reconstruction and abundance-estimation metrics.

Semi-Supervised Learning | Bohan Chen's Personal Webpage

GLL: A Differentiable Graph Learning Layer for Neural Networks

Graph-Based Active Learning for Nearly Blind Hyperspectral Unmixing