Author

Gabriel James Goenawan

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#machine learning May 2026

Attention Transfer Is Not Universally Effective for Vision Transformers

A comprehensive benchmark of 20 teachers from 11 well-known ViT families reveals that Attention Transfer is not universally effective, and identifies architectural mismatch between the pre-trained teacher and the standard student as the primary mechanism.

Huai-Yuan Qin, Mu-Li Yang, Gabriel James Goenawan et al. · arXiv.org · 3 citations

#artificial intelligence Preprint Sep 2026

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

Copying MLPs can serve as an effective foundation for Softmax-to-linear transfer: paired with the distilled attention, linear ViTs eventually close the remaining gap and even surpass Softmax ones.

Huai-Yuan Qin, Mu-Li Yang, Gabriel James Goenawan et al. · 1 citation

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