Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
Authors: Minjun Kim, Jongjin Kim, U Kang
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that SYNQ provides the state-of-the-art accuracy, over existing ZSQ methods. |
| Researcher Affiliation | Academia | Minjun Kim, Jongjin Kim & U Kang Seoul National University, Seoul, South Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 Quantization procedure of SYNQ |
| Open Source Code | Yes | Reproducibility. All of our implementation and datasets are available at https://github.com/snudm-starlab/Syn Q. |
| Open Datasets | Yes | We evaluate our method across three datasets by reporting the top-1 accuracy for the validation sets of CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Image Net (ILSVRC 2012) (Deng et al., 2009) datasets. |
| Dataset Splits | Yes | We evaluate our method across three datasets by reporting the top-1 accuracy for the validation sets of CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Image Net (ILSVRC 2012) (Deng et al., 2009) datasets. |
| Hardware Specification | Yes | All of our experiments were done at a workstation with Intel Xeon Silver 4214 and RTX 3090. |
| Software Dependencies | No | We implement SYNQ with Py Torch and Torch Vision libraries in Python. |
| Experiment Setup | Yes | We generate 5,120 images with a batch size of 256. The batch size for fine-tuning is 256 for CIFAR-10/100 and 16 for Image Net with epochs uniformly set to 100. We search τ, D0, λCE, and λCAM within the ranges {0.5, 0.55, 0.6, 0.65, 0.7}, {20, 40, 60, 80, 100}, {0.005, 0.05, 0.5, 5}, and {20, 50, 100, 200, 300, 500, 2000}, respectively. All of our experiments were done at a workstation with Intel Xeon Silver 4214 and RTX 3090. ... For the fine-tuning of the quantized model, the procedure follows Equation 6, employing SGD with a momentum of 0.9 and a weight decay of 1e-4. The batch size is set to 256 for CIFAR-10/100 and 16 for Image Net. Initial learning rate is searched within the range of {1e-4, 1e-5, 1e-6} and is decayed by a factor of 0.1 over training epochs nep = 100. |