PCGAN: Partition-Controlled Human Image Generation
Authors: Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou8698-8705
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Market-1501 and Deep Fashion datasets show that our model not only generates realistic human images but also produce the human pose and background as we want. Extensive experiments on COCO and LIP datasets indicate the potential of our method. |
| Researcher Affiliation | Academia | Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences School of Cyber Security, University of Chinese Academy of Sciences {liangdong, wangrui, tianxiaowei, zoucong}@iie.ac.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code can be found at https://github.com/Alan-IIE/PCGAN. |
| Open Datasets | Yes | We use the person re-identification dataset Market-1501 (Zheng et al. 2015), ... We also conduct experiments with a high-resolution dataset Deep Fashion (In-shop Clothes Retrieval Benchmark) (Liu et al. 2016), ... The COCO2017 dataset (Lin et al. 2014) is a large-scale dataset for multiple computer vision tasks including segmentation. The LIP (Liang et al. 2015) is a dataset focusing on the semantic understanding of person. |
| Dataset Splits | No | The paper does not provide specific validation dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and optimizers (e.g., 'Adam optimizer', 'Mask-RCNN') but does not specify their version numbers or other software dependencies required to replicate the experiment. |
| Experiment Setup | Yes | We train all the networks with mini-batch Adam optimizer (learning rate: 2e-4, β1 = 0.5, β2 = 0.999). The generator is designed in U-net structure with two encoders and one decoder. Our encoder is set as 7 blocks for the Deep Fashion dataset and 6 blocks for the Market-1501 dataset. ... The network is trained 90 epochs with 500 iterations. In each iteration, we typically set the critic per iteration per generator iteration to 2. The discriminator is trained 2 steps before the generator... |