Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated by synthetic data perform close to those calibrated by real data and even outperform them on W4A4.
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation
Hong Kong University of Science and Technology（香港科技大学）；Carnegie Mellon University（卡内基梅隆大学）；Reality Labs, Meta Inc.（Meta公司现实实验室）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Forward and Backward Information Retention for Accurate Binary Neural Networks
State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；SenseTime Research（商汤科技研究院）；Center for Future Media, University of Electronic Science and Technology of China（电子科技大学未来媒体中心）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University（北京航空航天大学大数据精准医疗北京实验室（注：此处机构为“北京航空航天大学大数据精准医疗北京实验室”，但原文Affiliation后未明确作者姓名，根据上下文推测可能为最后一位作者补充，严格按原文提取））
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization
Shanghai Jiao Tong University（上海交通大学）；South University of Science and Technology（南方科技大学）；University of California, Riverside（加州大学河滨分校）
由于在视频检测和分类中，当暴露于高维输入时需要大量的参数，因此在终端设备处开发紧凑而准确的视频理解是一个巨大的挑战。目前的工作集中在优化的视频检测和分类在一个单独的方式。在本文中，我们介绍了一个视频理解（目标检测和动作识别）系统的终端设备，即DEEPEYE。基于You Only Look Once（YOLO），我们在训练YOLO时开发了一种8位量化方法;还开发了由YOLO提取的特征组成的递归神经网络（RNN）的张量压缩方法。所开发的量化和张量化可以显着压缩原始网络模型，但保持精度。以MOMENTS和UCF 11视频数据集为基准，实验结果表明，在mAP仅降低0.47%的情况下，DEEPEYE实现了3.994倍的模型压缩率;在精度提高16.58%的情况下，参数减少15,047倍，速度提高2.87倍。
ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers
School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University（中山大学深圳校区电子与通信工程学院）；Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程学院）；University of Oxford（牛津大学）
DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Sun Yat-sen University（中山大学）；University of Science and Technology of China（中国科学技术大学）；University of Central Florida（中佛罗里达大学）；Nanjing University of Science and Technology（南京理工大学）；Microsoft Research（微软研究院）
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
Tsinghua University（清华大学）；State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）
AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization
Department of Electronics and Electrical Engineering, Keio University（庆应义塾大学电子与电气工程系）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）
Quantization of Deep Neural Networks for Accurate Edge Computing
Easylink Technology Co., Ltd（易联科技有限公司）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Cardiovascular Institute（广东省心血管病研究所）；Guangdong Academy of Medical Sciences（广东省医学科学院）；University of Notre Dame（圣母大学）
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
S$^2$Q-VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；ETH Zürich（苏黎世联邦理工学院）
