Data Free Quantization

13 papers with code • 2 benchmarks • 1 datasets

Data Free Quantization is a technique to achieve a highly accurate quantized model without accessing any training data.

Source: Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples


Use these libraries to find Data Free Quantization models and implementations


Most implemented papers

Data-Free Quantization Through Weight Equalization and Bias Correction

jakc4103/DFQ ICCV 2019

This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call.

ZeroQ: A Novel Zero Shot Quantization Framework

amirgholami/ZeroQ CVPR 2020

Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0. 5\% of one epoch training time of ResNet50 on ImageNet).

Generative Low-bitwidth Data Free Quantization

xushoukai/GDFQ ECCV 2020

More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method.

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

iamkanghyunchoi/qimera NeurIPS 2021

We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries.

Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization

htqin/dsg 1 Sep 2021

We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels.

SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation

clevercool/SQuant ICLR 2022

This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.

Patch Similarity Aware Data-Free Quantization for Vision Transformers

zkkli/psaq-vit 4 Mar 2022

The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters.

It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher

iamkanghyunchoi/ait CVPR 2022

To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks.

PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers

zkkli/psaq-vit 13 Sep 2022

In this paper, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT.

Genie: Show Me the Data for Quantization

SamsungLabs/Genie CVPR 2023

We also propose a post-training quantization algorithm to enhance the performance of quantized models.