NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
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The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization.
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NoisyQuant introduces a quantizer-agnostic method that enhances quantization by incorporating a Noisy Bias into input activations prior to quantization.
“NoisyQuant [55] introduces a quantizer-agnostic method that enhances the quantization process by incorporating a Noisy Bias into the input activations prior to quantization.”
From Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey (2024) · cites this paper as [55]
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Qian, J. P., Wang, P., Zhuge, Z., Hu, Q. P., & Cheng, J. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00043
HAP: Accelerating DNNs with Resolution-Preserved Quantization by Harnessing Adaptive-Precision
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Single domain generalized polyp detection in colonoscopy scene utilizing vision foundation models
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BLOB-Q: Boosting Low Bit ViT Quantization via Global Optimization on Model Distortion
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Stabilizing Ultra-Low-Bit Quantization of Multimodal LLMs via Global Bit Allocation
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CASQ: Configuration-Aware Sensitivity for Mixed-Precision Quantization of Vision Transformers
Kričković, L., & Kakani, V. (2026) · Electronics · doi:10.3390/electronics15194573
SDA-SAM: Semantic-Driven Adaptive Mixed-Precision Quantization for Segment Anything Model
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- Authors
- Liu, Y., Yang, H., Dong, Z., Keutzer, K., Du, L., & Zhang, S.
- Affiliations
- Nanjing University (CN)
- University of California, Berkeley (US)
- Peking University (CN)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 20321-20330
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075763
- Cited by
- 60
- References
- 48
- Citations by year
- 2026: 14 · 2025: 31 · 2024: 10 · 2023: 5
- FWCI
- 6.42
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2022YFB4400900)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- post-training quantization, activation quantization, vision transformer, quantization error reduction, uniform quantizer, model compression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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