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List of papers related to neural network quantization in recent AI conferences and journals.
| Date | Stars |
|---|---|
| 2026-07-31 | 839 |
| 2026-08-01 | 839 |
| 2026-08-02 | 839 |
| 2026-08-04 | 840 |
| 2026-08-06 | 840 |
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# Awesome-Quantization-Papers [](https://awesome.re)
This repo contains a comprehensive paper list of **Model Quantization** for efficient deep learning on AI conferences/journals/arXiv. As a highlight, we categorize the papers in terms of model structures and application scenarios, and label the quantization methods with keywords. <br>
This repo is being actively updated, and contributions in any form to make this list more comprehensive are welcome. Special thanks to collaborator [Zhikai Li](https://github.com/zkkli), and all researchers who have contributed to this repo! <br>
If you find this repo useful, please consider **★STARing** and feel free to share it with others! <br>
**[Update: Mar, 2024]** Add new papers from ICLR-25. <br>
**[Update: Nov, 2024]** Add new papers from ECCV-24 and NeurIPS-24. <br>
**[Update: Sep, 2024]** Add new papers from ICML-24 and IJCAI-24. <br>
**[Update: Jul, 2024]** Add new papers from CVPR-24. <br>
**[Update: May, 2024]** Add new papers from ICLR-24. <br>
**[Update: Apr, 2024]** Add new papers from AAAI-24. <br>
**[Update: Nov, 2023]** Add new papers from NeurIPS-23. <br>
**[Update: Oct, 2023]** Add new papers from ICCV-23. <br>
**[Update: Jul, 2023]** Add new papers from AAAI-23 and ICML-23. <br>
**[Update: Jun, 2023]** Add new arXiv papers uploaded in May 2023, especially the hot LLM quantization field. <br>
**[Update: Jun, 2023]** Reborn this repo! New style, better experience! <br>
---
## Overview
- [Awesome-Quantization-Papers ](#awesome-quantization-papers-)
- [Overview](#overview)
- [Survey](#survey)
- [Transformer-based Models](#transformer-based-models)
- [Language Transformers](#language-transformers)
- [Vision Transformers](#vision-transformers)
- [Visual Generation](#visual-generation)
- [Convolutional Neural Networks](#convolutional-neural-networks)
- [Visual Generation](#visual-generation-1)
- [Image Classification](#image-classification)
- [Other Tasks](#other-tasks)
- [Object Detection](#object-detection)
- [Super Resolution](#super-resolution)
- [Point Cloud](#point-cloud)
- [References](#references)
**Keywords**: **`PTQ`**: post-training quantization | **`Non-uniform`**: non-uniform quantization | **`MP`**: mixed-precision quantization | **`Extreme`**: binary or ternary quantization
---
## Survey
- "A Survey of Quantization Methods for Efficient Neural Network Inference", Book Chapter: Low-Power Computer Vision, 2021. [[paper](https://arxiv.org/abs/2103.13630)]
- "Full Stack Optimization of Transformer Inference: a Survey", arXiv, 2023. [[paper](https://arxiv.org/abs/2302.14017)]
- "A White Paper on Neural Network Quantization", arXiv, 2021. [[paper](https://arxiv.org/abs/2106.08295)]
- "Binary Neural Networks: A Survey", PR, 2020. [[Paper](https://arxiv.org/abs/2004.03333)] [**`Extreme`**]
## Transformer-based Models
### Language Transformers
- "CBQ: Cross-Block Quantization for Large Language Models", ICLR, 2025. [[paper](https://iclr.cc/virtual/2025/poster/28924)]
- "SpinQuant: LLM Quantization with Learned Rotations", ICLR, 2025. [[paper](https://iclr.cc/virtual/2025/poster/28338)]
- "LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid", ICLR, 2025. [[paper](https://iclr.cc/virtual/2025/poster/30168)]
- "Q-VLM: Post-training Quantization for Large Vision-Language Models", NeurIPS, 2024. [[paper](https://nips.cc/virtual/2024/poster/94107)]
- "KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization", NeurIPS, 2024. [[paper](https://nips.cc/virtual/2024/poster/96936)]
- "QBB: Quantization with Binary Bases for LLMs", NeurIPS, 2024. [[paper](https://nips.cc/virtual/2024/poster/95634)]
- "DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs", NeurIPS, 2024. [[paper](https://nips.cc/virtual/2024/poster/93727)]
- "ZipCache: Accurate and Efficient KV Cache Quantization withExcerpt of 55,519 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ca9a90879cef7f88, topic:quantization, topic:model-compression, name:quantization
matched fp:ca9a90879cef7f88, topic:awesome-list, topic:papers