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..
Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data
Authors: Shangyu Chen, Wenya Wang, Sinno Jialin Pan3329-3336
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark deep models are conducted to demonstrate the effectiveness of our proposed method using 1% of CIFAR10 and Image Net datasets. |
| Researcher Affiliation | Academia | Shangyu Chen,1 Wenya Wang,1 Sinno Jialin Pan1 1Nanyang Technological University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Layer-wise Unsupervised Network Quantization |
| Open Source Code | Yes | Codes are available in: https://github.com/csyhhu/L-DNQ |
| Open Datasets | Yes | Two benchmark datasets are used including Image Net ILSVRC-2012 and CIFAR-10. |
| Dataset Splits | Yes | 500 training instances in CIFAR-10 and 12,800 in Image Net are randomly sampled to simulate the scenario of limited instances. ... For fair comparison with training-based quantization, we reduce training data to 1% of the original training dataset. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU model, CPU type) used for running the experiments. |
| Software Dependencies | No | The paper mentions general tools and frameworks (e.g., 'deep learning framework'), but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | 500 training instances in CIFAR-10 and 12,800 in Image Net are randomly sampled to simulate the scenario of limited instances. All experiments are conducted 5 times and the average result is reported. ... For fair comparison with training-based quantization, we reduce training data to 1% of the original training dataset. ... L-DNQ adopts the following quantization intervals: âŠl = αl {0, 20, 21, 22... 2b} for each layer. |