Copy the Same, Distill the Difference: Initializing Linear Vision Transformers

In 30 Sekunden

Was passiert ist

Researchers show how to initialize linear Vision Transformers by copying MLP weights from Softmax ViTs and distilling attention behavior rather than copying attention weights directly.

Warum es zählt

Matters for engineers optimizing vision models who want faster inference by switching from standard to linear attention without full retraining.

Achtung

Method tested on various linear ViT variants and datasets, but real-world deployment performance across different domains remains to be validated.