GLL: A Differentiable Graph Learning Layer for Neural Networks
The GLL introduces a differentiable layer that integrates graph learning directly into neural networks to improve classification beyond standard methods. It uses adjoint backpropagation to precisely combine similarity graph construction and Laplacian propagation, replacing traditional projection heads. This results in better performance, smoother training, and significantly higher robustness against adversarial attacks compared to existing models.