PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Authors: Yikang LI, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluated on Visual Genome and COCO-Stuff dataset, our proposed method significantly outperforms the SOTA methods on Inception Score, Diversity Score and Fréchet Inception Distance. Extensive experiments also demonstrate our method s ability to generate complex and diverse images with given objects. |
| Researcher Affiliation | Collaboration | Yikang Li1 , Tao Ma2 , Yeqi Bai3, Nan Duan4, Sining Wei4, Xiaogang Wang1 1The Chinese University of Hong Kong, 2Northwestern Polytechnical University 3Nanyang Technological University, 4Microsoft |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Diagrams and mathematical formulas are present, but not pseudocode. |
| Open Source Code | Yes | The code is available at https://github.com/yikang-li/Paste GAN. |
| Open Datasets | Yes | COCO-Stuff [28] and Visual Genome [25] are two datasets used by previous scene image generation models [4, 17]. |
| Dataset Splits | Yes | Table 1 displays the attributes of the datasets. (Train 74121 / Val. 1024 / Test 2048 for COCO; Train 62565 / Val. 5506 / Test 5088 for VG) |
| Hardware Specification | Yes | training takes about 3 4 days on a single Tesla Titan X. |
| Software Dependencies | No | The paper mentions using "Adam [29]" as an optimizer but does not specify version numbers for other key software components or libraries (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | We train all models using Adam [29] with learning rate 5e-4 and batch size of 32 for 200,000 iterations; training takes about 3 4 days on a single Tesla Titan X. The λ1 λ8 are set to 1, 10, 1, 1, 1, 1, 0.5 and 10 respectively. |