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..
OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization
Authors: Peng Hu, Xi Peng, Hongyuan Zhu, Mohamed M. Sabry Aly, Jie Lin7780-7788
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on Image Net with Alex Net/Mobile Net-V1/Res Net-50 show that our method improves accuracy and training efficiency while obtains significantly higher compression rates compared to the state-of-the-art. |
| Researcher Affiliation | Academia | 1Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 2College of Computer Science, Sichuan University, Chengdu 610065, China 3Nanyang Technological University, Singapore |
| Pseudocode | Yes | Algorithm 1 Optimization process of our method Input: A pre-trained FP32 model with L layers, objective pruning rate p , objective quantization bitwidth B, batch size Nb, and maximum epoch number Ne. Output: Finetuned compressed model. 1: Compute the pruning masks {Mi}L i=1 for all layers (see Section 3.2). 2: Calculate the qunatization steps { i}L i for all layer (see Section 3.3). 3: for 1, 2, , Ne do 4: repeat 5: Randomly sample a minbatch from the training set. 6: Compress the weights using { i}L i and {Mi}L i=1 for the model. 7: Forward propagate with the pruned and quantized weights, and compute the cross entropy loss. 8: Update the model weights with descending their stochastic gradient. 9: until all samples selected 10: end for |
| Open Source Code | No | The paper does not provide a direct link to open-source code or explicitly state that the code will be made available. |
| Open Datasets | Yes | All experiments are performed on Image Net (i.e., ILSVRC-2012) (Deng et al. 2009), a large-scale image classification dataset consisted of 1.2M training images and 50K validation images. |
| Dataset Splits | Yes | All experiments are performed on Image Net (i.e., ILSVRC-2012) (Deng et al. 2009), a large-scale image classification dataset consisted of 1.2M training images and 50K validation images. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments. |
| Software Dependencies | No | The proposed method is implemented by Py Torch. However, no specific version number for PyTorch or other software dependencies is provided. |
| Experiment Setup | Yes | We set the batch size as 256 for all models at finetuning stage. The SGD otpimizer is utilized to finetune the compressed models with the momentum (= 0.9), weight decay (= 10 4), and learning rate (= 0.005). |