A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
Li, Shuhui Qu, Florian Metze, and Emma Strubell
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Learning quantization-aware linear paths in weight space yields a midpoint whose direct quantization matches quantization-aware training performance without using straight-through estimators.
citing papers explorer
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Zero-Shot Quantization via Weight-Space Arithmetic
A quantization vector derived from a donor model via weight-space arithmetic can be added to a receiver model to improve post-PTQ Top-1 accuracy by up to 60 points in 3-bit settings without receiver-side QAT or data.
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Neural Network Quantization by Learning Low-Loss Subspaces
Learning quantization-aware linear paths in weight space yields a midpoint whose direct quantization matches quantization-aware training performance without using straight-through estimators.