semantic_scholarProceedings of the International Conference on Neuromorphic Systems2026-08-04
Saliency-Informed Sparsity For Gating Deep Convolutional Feature Space
Shira Goldhaber-Gordon, A. Akwaboah, Aaron L. Sampson, R. Etienne-Cummings, Andreas G. Andreou, S. Mihalas, et al.
TL;DR: This work adopts a biologically plausible saliency-informed dropout technique as an explainable alternative to the unstructured standard random dropout approach and presents empirical results on regimes where such structured dynamic and static sparsities interact optimally to prune a ResNet model on the Imagenette dataset.
Biological vision has evolved to make efficient use of the limited information processing capability and tight energy budget of the brain by preferentially processing the most salient features of visual scenes. In contrast, modern deep vision models rely on expansive, high-dimens…