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..
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
Authors: Baozhou Zhu, Peter Hofstee, Johan Peltenburg, Jinho Lee, Zaid Alars
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that using generators discovered by the Auto Recon method always improve the performance of data-free compression. |
| Researcher Affiliation | Collaboration | 1Delft University of Technology, Delft, The Netherlands 2National University of Defense Technology, Changsha, China 3IBM Austin, Austin, TX, USA 4Yonsei University, Seoul, Korea |
| Pseudocode | Yes | Algorithm 1 The Auto Re Conmethod for data-free compression |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link to the source code for the methodology described. |
| Open Datasets | Yes | We use the same experimental settings as the GDFQ method... for both CIFAR-100 and Image Net classification |
| Dataset Splits | No | The paper mentions 'Lval r and Ltrain r refer to the reconstruction loss function on the reconstructed training dataset and the reconstructed validation dataset, respectively.' but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) for reproducibility of the overall experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments (e.g., specific GPU models, CPU models, or memory details). |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper states 'We use the same experimental settings as the GDFQ method' but does not explicitly list specific hyperparameter values or training configurations within this paper. |