Task-Aware Compressed Sensing With Generative Adversarial Networks
Authors: Maya Kabkab, Pouya Samangouei, Rama Chellappa
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our method on a variety of reconstruction and classification problems. |
| Researcher Affiliation | Academia | Maya Kabkab, Pouya Samangouei, Rama Chellappa Department of Electrical and Computer Engineering University of Maryland Institute for Advanced Computer Studies University of Maryland, College Park, MD 20742 {mayak, pouya, rama}@umiacs.umd.edu |
| Pseudocode | Yes | Algorithm 1 Task-aware GAN training algorithm. |
| Open Source Code | Yes | Our code has been made publicly available at https://github.com/po0ya/csgan. |
| Open Datasets | Yes | In our experiments, we use three different image datasets: the MNIST handwritten digits dataset [Le Cun et al., 1998], the Fashion-MNIST (F-MNIST) clothing articles dataset [Xiao, Rasul, and Vollgraf, 2017], and the Celeb Faces Attributes dataset (Celeb A) [Liu et al., 2015]. |
| Dataset Splits | Yes | The MNIST and F-MNIST datasets each consists of 60, 000 training images and 10, 000 testing images, each of size 28 28. We split the training images into a training set of 50, 000 images and hold-out a validation set containing 10, 000 images. The testing set is kept the same. |
| Hardware Specification | No | The paper mentions implementation details and hyperparameters are in the code repository, but does not specify the hardware used for experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | No | The paper states that 'Details of the hyper-parameters used in our experiments can be found in the code repository' but does not list them directly in the main text. |