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..
Feature Statistics Guided Efficient Filter Pruning
Authors: Hang Li, Chen Ma, Wei Xu, Xue Liu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive empirical experiments with various CNN architectures on publicly available datasets. The experimental results demonstrate that our model obtains up to 91.6% parameter decrease and 83.7% FLOPs reduction with almost no accuracy loss. |
| Researcher Affiliation | Academia | 1School of Computer Science, Mc Gill University 2Institute for Interdisciplinary Information Sciences, Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Our proposed filter pruning scheme; Algorithm 2 Similarity-aware feature map selection (SFS) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | CIFAR10 and CIFAR100 [Krizhevsky et al., 2009] are two widely used datasets with 32 × 32 colour natural images. They both contain 50, 000 training images and 10, 000 test images with 10 and 100 classes respectively. ... ILSVRC-2012 is a large-scale dataset with 1.2 million training images and 50, 000 validation images of 1000 classes. |
| Dataset Splits | Yes | For ILSVRC-2012, we use the pre-trained Res Net-50 released by Pytorch. We train Mobile Net for 60 epochs with a weight decay of 0.0015. ... ILSVRC-2012 is a large-scale dataset with 1.2 million training images and 50, 000 validation images of 1000 classes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'Pytorch' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | For CIFAR, we set the mini-batch size to 64, epochs to 160 with a weight decay of 0.0015 and Nesterov momentum [Sutskever et al., 2013] of 0.9. For ILSVRC-2012, we use the pre-trained Res Net-50 released by Pytorch. We train Mobile Net for 60 epochs with a weight decay of 0.0015. |