SAGL learns subspace-preserving sparse attention graphs from heterogeneous multiview data via bilinear attention and dynamic sparsity gating, outperforming state-of-the-art unsupervised transfer learning methods.
Attention transfer fails for four ViT families due to architectural mismatch, and adding the teacher's native components to students fully restores its effectiveness.
PLCI proposes a unified robust multi-view clustering framework that infers latent cross-view counterparts via posterior guidance to simultaneously handle incomplete views and noisy correspondences.