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..
Cooperative Pruning in Cross-Domain Deep Neural Network Compression
Authors: Shangyu Chen, Wenya Wang, Sinno Jialin Pan
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted to verify the effectiveness of our proposed method compared with several state-of-the-art approaches in the setting of limited training data. We conduct comparison experiments using the following baseline pruning methods: 1) LWC [Han et al., 2015], 2) OBD [Le Cun et al., 1990], 3) DNS [Guo et al., 2016], 4) LOBS [Dong et al., 2017]. Table 1: Overall results of CIFAR9-STL9 using CIFAR-Net. Table 2: Overall results of Image Net PASCAL, Image Net Caltech256, Image Net Bing using Image Net pre-trained Res Net18. CR is 4% for each layer. |
| Researcher Affiliation | Academia | Shangyu Chen , Wenya Wang and Sinno Jialin Pan Nanyang Technological University, Singapore EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Alg.1 illustrates the whole process of Co-Prune: |
| Open Source Code | Yes | Codes are available at https://github.com/csyhhu/Co-Prune. |
| Open Datasets | Yes | CIFAR9-STL9 is a modified version of combined CIFAR10 and STL10 dataset. ... Image CLEF is a 4-domain image dataset. It extracts 600 images of 12 classes from Image Net [Deng et al., 2009], Caltech-256 [Griffin et al., 2007], PASCAL [Everingham et al., 2010] and Bing, respectively. |
| Dataset Splits | No | Since data is quite limited in Image CLEF, we divide each domain into 80% for training and 20% for testing (with class balance). No explicit validation split is mentioned. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are mentioned in the paper regarding the experimental setup. |
| Software Dependencies | No | In practice, Adam [Kingma and Ba, 2014] with initial learning rate 10^-3 is used for Co-Prune and all retraining processes. This mentions an algorithm/optimizer but not specific software libraries or their versions. |
| Experiment Setup | Yes | In practice, Adam [Kingma and Ba, 2014] with initial learning rate 10^-3 is used for Co-Prune and all retraining processes. Learning rate will be divided by 10 when training loss increases for 3 consecutive epochs. Training to optimum is considered as learning rate becomes smaller than 10^-6. In Co-Prune, α0 = 0.7, αmin = 0.3, β = 3 for tradeoff between computational time and accuracy. |