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arXiv · 2609.07155

Mind the Approximation: Fisher-Weighted SVD Compression for ViTs

Moritz Thoma·Maximilian Groezinger·Maximilian Forstenhäusler·Emad Aghajanzadeh·Ryan Pegoud·Manoj Rohit Vemparala·Pierpaolo Mori·Alexander Frickenstein·Daniel Mueller-Gritschneder·Ulf Schlichtmann
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Abstract

Model compression is key to mitigate deployment challenges of ever growing machine learning models. In this area of research, singular value decomposition (SVD)-based compression offers a compelling trade-off between computational efficiency and model accuracy. Fisher-weighted SVD in particular provides principled, loss-aware compression. However, we find that improving the fidelity of Fisher approximation used in the compression is poorly predictive of post-compression accuracy for Vision Transformers (ViTs). Motivated by this observation, we propose FACTS, a structured Fisher Approximation tailored to Compressing ViTs with Fisher-weighted SVD, which enforces token-local aggregation while preserving within-token activation-gradient dependence. Additionally, we introduce a fast Constrained Rank Search (CoRS), that optimizes layer-wise rank allocation while adhering to a fixed floating point operation (FLOP) constraint. Extensive experiments across ViTs and hybrid architectures demonstrate that FACTS consistently improves accuracy-efficiency trade-offs without requiring finetuning. Notably, it outperforms the strongest SVD baseline by up to +5.8 percentage points (p.p.) Top-1 on Swin-B, with further gains driven by our search method. Code is available at https://github.com/MoritzTho/FACTS.

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Moritz Thoma, Maximilian Groezinger, Maximilian Forstenhäusler, Emad Aghajanzadeh, Ryan Pegoud, Manoj Rohit Vemparala, Pierpaolo Mori, Alexander Frickenstein, Daniel Mueller-Gritschneder, Ulf Schlichtmann. 2026-09-07. Mind the Approximation: Fisher-Weighted SVD Compression for ViTs. https://arxiv.org/abs/2609.07155

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