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..
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Authors: Yang Sui, Miao Yin, Yi Xie, Huy Phan, Saman Aliari Zonouz, Bo Yuan
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation results for different models on various datasets show the superior performance of our approach. Notably, on CIFAR-10 dataset our solution can bring 0.90% and 0.94% accuracy increase over baseline Res Net-56 and Res Net-110 models, respectively, and meanwhile the model size and FLOPs are reduced by 42.8% and 47.4% (for Res Net-56) and 48.3% and 52.1% (for Res Net-110), respectively. On Image Net dataset, our approach can achieve 40.8% and 44.8% storage and computation reductions, respectively, with 0.15% accuracy increase over the baseline Res Net-50 model. |
| Researcher Affiliation | Academia | Yang Sui Miao Yin Yi Xie Huy Phan Saman Zonouz Bo Yuan Department of Electrical and Computer Engineering Rutgers University Piscataway, NJ 08854, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 CHannel Independence-based Pruning (CHIP) procedure for the l-th layer Input: Pre-trained weight tensor Wl, N sets of feature maps Al = {Al 1, Al 2, , Al cl} Rcl h w from N input samples, and the desired number of filters to be preserved κl. Output: Pruned weight tensor Wl prune. 1: for each input sample do 2: Flatten feature maps: Al := reshape(Al, [cl, hw]); 3: for i = 1 to cl do 4: CI calculation: Calculate CI(Al i) via Equation 3; 5: end for 6: end for 7: Averaging: Average CI(Al i) under all N input samples; 8: Sorting: Sort {CI(Al i)}cl i=1 in ascending order; 9: Pruning: Prune cl κl filters in Wl corresponding to the cl κl smallest CI(Al i); 10: Fine-tuning: Obtain final Wl prune via fine-tuning Wl with removing the pruned filter channels. |
| Open Source Code | Yes | The code is available at https://github.com/Eclipsess/CHIP_NeurIPS2021. |
| Open Datasets | Yes | To be specific, we conduct experiments for three CNN models (Res Net-56, Res Net-110 and VGG-16) on CIFAR-10 dataset [24]. Also, we further evaluate our approach and compare its performance with other state-of-the-art pruning methods for Res Net-50 model on large-scale Image Net dataset [5]. |
| Dataset Splits | No | The paper mentions fine-tuning on CIFAR-10 and ImageNet datasets with specific batch sizes and epochs, but does not explicitly state the train/validation/test dataset splits (e.g., percentages or sample counts) used for reproducibility, nor does it explicitly confirm using standard validation splits. |
| Hardware Specification | Yes | We conduct our empirical evaluations on Nvidia Tesla V100 GPUs with Py Torch 1.7 framework. |
| Software Dependencies | Yes | We conduct our empirical evaluations on Nvidia Tesla V100 GPUs with Py Torch 1.7 framework. |
| Experiment Setup | Yes | To be specific, we perform the fine-tuning for 300 epochs on CIFAR-10 datasets with the batch size, momentum, weight decay and initial learning rate as 128, 0.9, 0.05 and 0.01, respectively. On the Image Net dataset, fine-tuning is performed for 180 epochs with the batch size, momentum, weight decay and initial learning rate as 256, 0.99, 0.0001 and 0.1, respectively. |