Network Deconvolution
Authors: Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermuller, Yiannis Aloimonos
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | 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 {cxy, mevanusa, huah, amitrokh}@umd.edu {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. |