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..
Network Deconvolution
Authors: Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermuller, Yiannis Aloimonos
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Cityscapes, and Image Net datasets. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Maryland, College Park EMAIL {tomg@cs,yorke@,fer@umiacs,yiannis@cs}.umd.edu |
| Pseudocode | Yes | Algorithm 1 Computing the Deconvolution Matrix |
| Open Source Code | Yes | Source code can be found at: https://github.com/yechengxi/deconvolution |
| Open Datasets | Yes | Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Cityscapes, and Image Net datasets. |
| Dataset Splits | No | The paper mentions training on various datasets and evaluating performance, but it does not explicitly specify exact training, validation, and test splits (e.g., percentages or sample counts for each partition). |
| Hardware Specification | No | The paper mentions 'CPU timing' in Table 3 but does not specify any particular CPU models, GPU models, or other hardware specifications used for the experiments. |
| Software Dependencies | No | The paper mentions adapting models from 'pytorch-cifar' and using models from the 'Py Torch model zoo', implying the use of PyTorch. However, no specific version numbers for PyTorch or any other software dependencies are provided. |
| Experiment Setup | Yes | For the convolutional layers, we set B = 64 before calculating the covariance matrix. For the fully-connected layers, we set B equal to the input channel number, which is usually 512. We set the batch size to 128 and the weight decay to 0.001. All models are trained with SGD and a learning rate of 0.1. |