Existing quantization aware training methods attempt to compensate for the quantization loss by leveraging on training data, like most of the post-training quantization methods, and are also time consuming. Both these methods are not effective for privacy constraint applications as they are tightly coupled with training data. In contrast, this paper proposes a data-independent post-training quantization scheme that eliminates the need for training data. This is achieved by generating a faux dataset, hereafter referred to as Retro-Synthesis Data, from the FP32 model layer statistics and further using it for quantization. This approach outperformed state-of-the-art methods including, but not limited to, ZeroQ and DFQ on models with and without Batch-Normalization layers for 8, 6, and 4 bit precisions on ImageNet and CIFAR-10 datasets. We also introduced two futuristic variants of post-training quantization methods namely Hybrid Quantization and Non-Uniform Quantization
3D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmark
Visual and Spatial AI Lab（视觉与空间人工智能实验室）；VCCM Section College of PVFA（VCCM学院光电工程学院）；Department of ECEN（电子工程系）；Department of CSCE（计算机科学与工程系）；Texas A&M University（德克萨斯A&M大学）
Long-Range Zero-Shot Generative Deep Network Quantization
Hefei University of Technology（合肥工业大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Zhengzhou University（郑州大学）
Efficient Non-Exemplar Class-Incremental Learning with Retrospective Feature Synthesis
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
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）
Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
CFAR, Agency for Science, Technology and Research, Singapore（新加坡科技研究局应用科学与技术研究院）；IHPC, Agency for Science, Technology and Research, Singapore（新加坡科技研究局信息与通信技术研究院）；National University of Singapore（新加坡国立大学）
