Robustness-Guided Image Synthesis for Data-Free Quantization
Authors: Jianhong Bai, Yuchen Yang, Huanpeng Chu, Hualiang Wang, Zuozhu Liu, Ruizhe Chen, Xiaoxuan He, Lianrui Mu, Chengfei Cai, Haoji Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments, showing that the proposed RIS outperforms various existing data-free quantization methods by a large margin, and can be further extended to data-free knowledge distillation. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Kuaishou Technology 3The Hong Kong University of Science and Technology 4Tencent Data Platform |
| Pseudocode | Yes | Algorithm 1: The synthesis process of our RIS scheme. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We evaluated the proposed RIS on CIFAR-10/100 (Krizhevsky, Hinton et al. 2009) and Image Net (Krizhevsky, Sutskever, and Hinton 2012). |
| Dataset Splits | No | The paper refers to standard datasets (CIFAR-10/100, ImageNet) but does not explicitly provide specific training/test/validation split percentages, sample counts, or explicit instructions for reproducing the data partitioning in the provided text. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | More training details can be found in Appendix D. |