Author

Gabriel James Goenawan

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#artificial intelligence Preprint Sep 2026

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

Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity attention operator for more efficient token routing, but they require from-scratch pre-training and typically underperform the original Softmax version. How to initialize linear ViTs both efficiently and e...

Huai-Yuan Qin, Mu-Li Yang, Gabriel James Goenawan et al. · 0 citations

Preprint Aug 2026

DiD It in 87 Minutes: A Label-Free Softmax-to-Linear Adaptation of Vision Transformers for Object Detection

DiD is introduced, a label-free conversion method that exclusively trains the linear-attention backbone by aligning detector-facing interface tensors with those of a frozen Softmax teacher, and substantially outperforms established baselines and matches supervised, fully trained linear models.

Huai-Yuan Qin, Gabriel James Goenawan, Zihang Lin et al. · 1 citation