Super-Resolution and Inpainting with Degraded and Upgraded Generative Adversarial Networks
Authors: Yawen Huang, Feng Zheng, Danyang Wang, Junyu Jiang, Xiaoqian Wang, Ling Shao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comparative evaluation demonstrates that the effectiveness of the proposed method on different brain MRI datasets. In addition, our method outperforms many existing super-resolution and inpainting approaches. We evaluate the proposed method on two publicly available datasets: IXI 1, and HCP 2, which include real acquired LQ/HQ data. The quantitative results are listed in Table 1. |
| Researcher Affiliation | Collaboration | Yawen Huang1,2 , Feng Zheng3,4 , Danyang Wang1,2 , Junyu Jiang1,2 , Xiaoqian Wang5 , Ling Shao6 1Malong Technologies 2Shenzhen Malong Artificial Intelligence Research Center 3Depatment of Computer Science and Technology, Southern University of Science and Technology 4Research Institute of Trustworthy Autonomous Systems 5Purdue University 6Inception Institute of Artificial Intelligence |
| Pseudocode | No | The paper describes the network architecture and loss functions, but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links to open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate the proposed method on two publicly available datasets: IXI 1, and HCP 2, which include real acquired LQ/HQ data. Specifically, the IXI dataset contains 578 healthy subjects acquired by a Philips 3T/1.5T system and a GE 1.5T system. One branch of the HCP dataset has a total of 200 subjects acquired via a Siemens 3T scanner. 1http://brain-development.org/ixi-dataset 2https://www.humanconnectome.org |
| Dataset Splits | No | The paper states: "We split the datasets into 500 (IXI) and 120 (HCP) for training, 78 (IXI) and 80 (HCP) for testing." It explicitly mentions training and testing splits, but no separate validation split. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., GPU/CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a "VGG19 network" and "Adam" optimizer, but it does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We use Adam with 105 iterations and a learning rate of 10 4, which is decayed by a factor of 2 every 2 105 minibatch updates. For the parameters, we set α = 10, δ = 10, β = 0.1, λ = 1. |