# Genie: Show Me the Data for Quantization

arXiv: https://arxiv.org/abs/2212.04780 · HTML: https://mlsanity.com/paper/2212.04780/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 43 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction](https://mlsanity.com/paper/2102.05426/) (2021)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [ZeroQ: A Novel Zero Shot Quantization Framework](https://mlsanity.com/paper/2001.00281/) (2020)
- [QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization](https://mlsanity.com/paper/2203.05740/) (2022)
- [Semantic Photo Manipulation with a Generative Image Prior](https://mlsanity.com/paper/2005.07727/) (2020)
- [Optimizing the Latent Space of Generative Networks](https://mlsanity.com/paper/1707.05776/) (2017)
- [Overcoming Oscillations in Quantization-Aware Training](https://mlsanity.com/paper/2203.11086/) (2022)
- [Gradient Inversion with Generative Image Prior](https://mlsanity.com/paper/2110.14962/) (2021)
- [Generative Low-bitwidth Data Free Quantization](https://mlsanity.com/paper/2003.03603/) (2020)
- [The Knowledge Within: Methods for Data-Free Model Compression](https://mlsanity.com/paper/1912.01274/) (2019)
- [Stochastic Pooling for Regularization of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1301.3557/) (2013)
- [Zero-shot Adversarial Quantization](https://mlsanity.com/paper/2103.15263/) (2021)
- [IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization](https://mlsanity.com/paper/2111.09136/) (2021)
- [Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples](https://mlsanity.com/paper/2111.02625/) (2021)
- [All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1903.05285/) (2019)
- [When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism](https://mlsanity.com/paper/2201.10801/) (2022)
- [MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing](https://mlsanity.com/paper/2011.09899/) (2020)

## Built on by (most cited first, 17 of 17)

- [BoA: Attention-aware Post-training Quantization without Backpropagation](https://mlsanity.com/paper/2406.13474/) (2024)
- [Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers](https://mlsanity.com/paper/2402.08958/) (2024)
- [GenQ: Quantization in Low Data Regimes with Generative Synthetic Data](https://mlsanity.com/paper/2312.05272/) (2023)
- [Zero-shot Quantization: A Comprehensive Survey](https://mlsanity.com/paper/2505.09188/) (2025)
- [EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization](https://mlsanity.com/paper/2309.11531/) (2023)
- [MetaAug: Meta-Data Augmentation for Post-Training Quantization](https://mlsanity.com/paper/2407.14726/) (2024)
- [Sharpness-Aware Data Generation for Zero-shot Quantization](https://mlsanity.com/paper/2510.07018/) (2025)
- [Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers](https://mlsanity.com/paper/2412.16553/) (2024)
- [Data Generation for Hardware-Friendly Post-Training Quantization](https://mlsanity.com/paper/2410.22110/) (2024)
- [Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction](https://mlsanity.com/paper/2505.00259/) (2025)
- [Enhancing Generalization in Data-free Quantization via Mixup-class Prompting](https://mlsanity.com/paper/2507.21947/) (2025)
- [SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning](https://mlsanity.com/paper/2603.18423/) (2026)
- [GranQ: Efficient Channel-wise Quantization via Vectorized Pre-Scaling for Zero-Shot QAT](https://mlsanity.com/paper/2503.18339/) (2025)
- [D4C: Data-Free Quantization for Contrastive Language-Image Pre-training Models](https://mlsanity.com/paper/2511.15411/) (2025)
- [Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2511.17801/) (2025)
- [Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models](https://mlsanity.com/paper/2602.01289/) (2026)
- [Toward Multi-Domain and Long-Tailed Quantization via Feature Alignment and Scaling](https://mlsanity.com/paper/2606.04920/) (2026)

## Abstract (arXiv)

Zero-shot quantization is a promising approach for developing lightweight deep neural networks when data is inaccessible owing to various reasons, including cost and issues related to privacy. By exploiting the learned parameters (μ and σ) of batch normalization layers in an FP32-pre-trained model, zero-shot quantization schemes focus on generating synthetic data. Subsequently, they distill knowledge from the pre-trained model (teacher) to the quantized model (student) such that the quantized model can be optimized with the synthetic dataset. However, thus far, zero-shot quantization has primarily been discussed in the context of quantization-aware training methods, which require task-specific losses and long-term optimization as much as retraining. We thus introduce a post-training quantization scheme for zero-shot quantization that produces high-quality quantized networks within a few hours. Furthermore, we propose a framework called Genie~that generates data suited for quantization. With the data synthesized by Genie, we can produce robust quantized models without real datasets, which is comparable to few-shot quantization. We also propose a post-training quantization algorithm to enhance the performance of quantized models. By combining them, we can bridge the gap between zero-shot and few-shot quantization while significantly improving the quantization performance compared to that of existing approaches. In other words, we can obtain a unique state-of-the-art zero-shot quantization approach. The code is available at urlhttps://github.com/SamsungLabs/Genie.