Mind the Approximation: Fisher-Weighted SVD Compression for ViTs

• URL: http://arxiv.org/abs/2609.07155v1
• Date: Mon, 07 Sep 2026 07:50:50 GMT
• Title: Mind the Approximation: Fisher-Weighted SVD Compression for ViTs
• Abstract summary: FACTS is a structured Fisher Approximation tailored to Vision Transformers (ViTs)<n>It enforces token-local aggregation while preserving within-token activation-gradient dependence.<n>It consistently improves accuracy-efficiency trade-offs without requiring finetuning.
• Score: 37.05832234320274
• License: http://creativecommons.org/licenses/by/4.0/
• 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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