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..
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Authors: Mikhail Figurnov, Aizhan Ibraimova, Dmitry P. Vetrov, Pushmeet Kohli
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that this method can reduce the evaluation time of modern CNN architectures proposed in the literature by a factor of 2 4 with a small decrease in accuracy. We use three convolutional neural networks of increasing size and computational complexity: Network in Network [17], Alex Net [14] and VGG-16 [25], see table 1. In all networks, we attempt to perforate all the convolutional layers, except for the 1 1 convolutional layers of NIN. We perform timings on a computer with a quad-core Intel Core i5-4460 CPU, 16 GB RAM and a n Vidia Geforce GTX 980 GPU. The results are presented in table 3. |
| Researcher Affiliation | Collaboration | 1National Research University Higher School of Economics 2Lomonosov Moscow State University 3Yandex 4Skolkovo Institute of Science and Technology 5Microsoft Research |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/mfigurnov/perforated-cnn-matconvnet, https://github.com/mfigurnov/perforated-cnn-caffe. |
| Open Datasets | Yes | We use three convolutional neural networks of increasing size and computational complexity: Network in Network [17], Alex Net [14] and VGG-16 [25], see table 1. In all networks, we attempt to perforate all the convolutional layers, except for the 1 1 convolutional layers of NIN. Network Dataset Error CPU time GPU time Mem. Mult. # conv NIN CIFAR-10 top-1 10.4%... Alex Net Image Net top-5 19.6%... VGG-16 top-5 10.1%... |
| Dataset Splits | No | The paper mentions 'training dataset' and 'training images' and uses performance metrics like 'error increase' and 'speedup'. However, it does not explicitly describe a validation set or specific splits for training, validation, and testing, nor does it detail how hyperparameters were tuned using a validation set. Therefore, it does not provide specific dataset split information for validation needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We perform timings on a computer with a quad-core Intel Core i5-4460 CPU, 16 GB RAM and a n Vidia Geforce GTX 980 GPU. |
| Software Dependencies | No | For Alex Net, the Caffe reimplementation is used which is slightly different from the original architecture (pooling and normalization layers are swapped). We use a fork of Mat Conv Net framework for all experiments, except for ο¬ne-tuning of Alex Net and VGG-16, for which we use a fork of Caffe. The source code is available at https://github.com/mfigurnov/perforated-cnn-matconvnet, https://github.com/mfigurnov/perforated-cnn-caffe. The paper mentions specific software frameworks (Caffe, Mat Conv Net) but does not provide their version numbers. |
| Experiment Setup | Yes | The batch size used for timings is 128 for NIN, 256 for Alex Net and 16 for VGG-16. We use twenty perforation rates: 1 3, . . . , 18 20. In order to decrease the error of the accelerated network, we tune the network s weights. We do not observe any problems with backpropagation, such as exploding/vanishing gradients. The results are presented in table 3. Finally, we perform the second round of ο¬ne-tuning with a much lower learning rate of 1e-9, due to exploding gradients. |