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..
Attribution Preservation in Network Compression for Reliable Network Interpretation
Authors: Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm both quantitatively and qualitatively on diverse compression methods. |
| Researcher Affiliation | Collaboration | KAIST1, AITRICS2, South Korea |
| Pseudocode | No | The paper presents its framework and methods using mathematical formulations and descriptive text, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a repository link or a statement about code being available in supplementary materials. |
| Open Datasets | Yes | We utilize the held out 1,449 images with segmentation masks in the PASCAL VOC 2012 dataset. [28] |
| Dataset Splits | No | The paper mentions training on the PASCAL VOC 2012 dataset but does not specify explicit training/validation splits or percentages required to reproduce the experiment setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | For unstructured pruning...We use pruning rate ρw = 0.2. After pruning is complete, the remaining sparse network is fine-tuned for 30 epochs on the same dataset. The whole process is then iterated 16 times to produce the final compressed network with pruning rate ρ = 0.97. ... For structured pruning, we use the ℓ1 structured pruning proposed in [2], in which whole filters are pruned according to the magnitude of each filter s ℓ1 norm. ... We use channel pruning rate ρc = 0.7. |