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arXiv · 2602.18116

Cut Less, Fold More: Model Compression through the Lens of Projection Geometry

Olga Saukh·Dong Wang·Haris Šikić·Yun Cheng·Lothar Thiele
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Abstract

Compressing neural networks without retraining is vital for deployment at scale. We study calibration-free compression through the lens of projection geometry: structured pruning is an axis-aligned projection, whereas model folding performs a low-rank projection via weight clustering. We formalize both as orthogonal operators and show that, within a rank distance of one, folding provably yields smaller parameter reconstruction error, and under mild smoothness assumptions, smaller functional perturbations than pruning. At scale, we evaluate >1000 checkpoints spanning ResNet18, PreActResNet18, ViT-B/32, and CLIP ViT-B/32 on CIFAR-10 and ImageNet-1K, covering diverse training hyperparameters (optimizers, learning rates, augmentations, regularization, sharpness-aware training), as well as multiple LLaMA-family 60M and 130M parameter models trained on C4. We show that folding typically achieves higher post-compression accuracy, with the largest gains at moderate-high compression. The gap narrows and occasionally reverses at specific training setups. Our results position folding as a geometry-aware, calibration-free alternative to pruning that is often superior in practice and principled in theory.

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Olga Saukh, Dong Wang, Haris Šikić, Yun Cheng, Lothar Thiele. 2026-02-20. Cut Less, Fold More: Model Compression through the Lens of Projection Geometry. https://arxiv.org/abs/2602.18116

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