Data to 7 Oct 2026

Data-Free Quantization Through Weight Equalization and Bias Correction

arXiv 1906.04721 ↗First posted 11 Jun 2019cs.LG cs.CV stat.MLcited by 328

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Abstract (arXiv)

We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning hardware. However, quantizing models to run in 8-bit is a non-trivial task, frequently leading to either significant performance reduction or engineering time spent on training a network to be amenable to quantization. Our approach relies on equalizing the weight ranges in the network by making use of a scale-equivariance property of activation functions. In addition the method corrects biases in the error that are introduced during quantization. This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call. For common architectures, such as the MobileNet family, we achieve state-of-the-art quantized model performance. We further show that the method also extends to other computer vision architectures and tasks such as semantic segmentation and object detection.

Key facts. Cited by 328 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 51; 2022: 55; 2023: 74; 2024: 55; 2025: 54; 2026: 39.

328 papers.

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2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,338
2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
2023H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Modelscited by 805
2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
2022ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformerscited by 517
2021BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstructioncited by 464
2021A White Paper on Neural Network Quantizationcited by 453
2024SpinQuant: LLM quantization with learned rotationscited by 446
2023QuIP: 2-Bit Quantization of Large Language Models With Guaranteescited by 397
2022Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruningcited by 317
2021Post-Training Quantization for Vision Transformercited by 274
2022The case for 4-bit precision: k-bit Inference Scaling Lawscited by 247

38 more

2023Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Timecited by 247
2022QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantizationcited by 175
2021PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantizationcited by 173
2023PTQD: Accurate Post-Training Quantization for Diffusion Modelscited by 167
2021Implicit Neural Representations for Image Compressioncited by 161
2023Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothingcited by 147
2021Understanding and Overcoming the Challenges of Efficient Transformer Quantizationcited by 139
2022RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformerscited by 134
2022Overcoming Oscillations in Quantization-Aware Trainingcited by 116
2023OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Modelscited by 113
2024Sequoia: Scalable, Robust, and Hardware-aware Speculative Decodingcited by 103
2022FP8 Quantization: The Power of the Exponentcited by 87
2022Fisher SAM: Information Geometry and Sharpness Aware Minimisationcited by 84
2022COIN++: Neural Compression Across Modalitiescited by 81
2023Temporal Dynamic Quantization for Diffusion Modelscited by 81
2023LLM-FP4: 4-Bit Floating-Point Quantized Transformerscited by 65
2021Diversifying Sample Generation for Accurate Data-Free Quantizationcited by 63
2023QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Modelscited by 63
2023Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scalingcited by 62
2021VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inferencecited by 59
2023Oscillation-free Quantization for Low-bit Vision Transformerscited by 55
2025GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibrationcited by 54
2021Implicit Neural Video Compressioncited by 52
2022SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximationcited by 51
2023Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMscited by 49
2023FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantizationcited by 46
2023Pruning vs Quantization: Which is Better?cited by 45
2021Zero-shot Adversarial Quantizationcited by 44
2021IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantizationcited by 43
2024ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Modelscited by 43
2024Low-Rank Quantization-Aware Training for LLMscited by 42
2021Qimera: Data-free Quantization with Synthetic Boundary Supporting Samplescited by 39
2021MQBench: Towards Reproducible and Deployable Model Quantization Benchmarkcited by 38
2021OMPQ: Orthogonal Mixed Precision Quantizationcited by 37
2021Towards Efficient Post-training Quantization of Pre-trained Language Modelscited by 37
2024BitsFusion: 1.99 bits Weight Quantization of Diffusion Modelcited by 37
2022Q-ViT: Fully Differentiable Quantization for Vision Transformercited by 36
2025Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantizationcited by 36

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