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..
Dynamic Channel Pruning: Feature Boosting and Suppression
Authors: Xitong Gao, Yiren Zhao, Łukasz Dudziak, Robert Mullins, Cheng-zhong Xu
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran extensive experiments on CIFAR-10 (Krizhevsky et al., 2014) and the Image Net ILSVRC2012 (Deng et al., 2009), two popular image classification datasets. ... Empirical results show that under the same speed-ups, FBS can produce models with validation accuracies surpassing all other channel pruning and dynamic conditional execution methods examined in the paper. |
| Researcher Affiliation | Academia | 1 Shenzhen Institutes of Advanced Technology, Shenzhen, China 2,3,4 University of Cambridge, Cambridge, UK 5 University of Macau, Macau, China 1 EMAIL, 2 EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Finally, the implementation of FBS and the optimized networks are fully open source and released to the public1. 1https://github.com/deep-fry/mayo |
| Open Datasets | Yes | We ran extensive experiments on CIFAR-10 (Krizhevsky et al., 2014) and the Image Net ILSVRC2012 (Deng et al., 2009), two popular image classification datasets. |
| Dataset Splits | Yes | Empirical results show that under the same speed-ups, FBS can produce models with validation accuracies surpassing all other channel pruning and dynamic conditional execution methods examined in the paper. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'conventional stochastic gradient descent' for training but does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | We trained M-Cifar Net (see Appendix A) with a 0.01 learning rate and a 256 batch size. We reduced the learning rate by a factor of 10 for every 100 epochs. ... ILSVRC2012 classifiers, i.e. Res Net-18 and VGG-16, were trained with a procedure similar to Appendix A. The difference was that they were trained for a maximum of 35 epochs, the learning rate was decayed for every 20 epochs, and NS models were all pruned at 15 epochs. |