You Only Need Adversarial Supervision for Semantic Image Synthesis
Authors: Edgar Schönfeld, Vadim Sushko, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on three challenging datasets: ADE20K (Zhou et al., 2017), COCO-stuff (Caesar et al., 2018) and Cityscapes (Cordts et al., 2016). Following prior work (Isola et al., 2017; Wang et al., 2018; Park et al., 2019; Liu et al., 2019), we evaluate models quantitatively on the validation set using the Fr echet Inception Distance (FID) (Heusel et al., 2017) and mean Intersection-over-Union (m Io U). |
| Researcher Affiliation | Collaboration | Edgar Sch onfeld Bosch Center for Artificial Intelligence Vadim Sushko * Bosch Center for Artificial Intelligence Dan Zhang Bosch Center for Artificial Intelligence J urgen Gall University of Bonnartificialintelligence Bernt Schiele Max Planck Institute for Informaticsartificialintelligen Anna Khoreva Bosch Center for Artificial Intelligence |
| Pseudocode | No | The paper provides detailed architectural tables for the discriminator and generator, but it does not include any blocks or sections explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Our code and pretrained models are available at https://github.com/boschresearch/OASIS. |
| Open Datasets | Yes | We conduct experiments on three challenging datasets: ADE20K (Zhou et al., 2017), COCO-stuff (Caesar et al., 2018) and Cityscapes (Cordts et al., 2016). |
| Dataset Splits | Yes | Following prior work (Isola et al., 2017; Wang et al., 2018; Park et al., 2019; Liu et al., 2019), we evaluate models quantitatively on the validation set using the Fr echet Inception Distance (FID) (Heusel et al., 2017) and mean Intersection-over-Union (m Io U). |
| Hardware Specification | Yes | All the experiments were run on 4 Tesla V100 GPUs, with a batch size of 20 for Cityscapes, and 32 for ADE20k and COCO-Stuff. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for software dependencies such as programming languages, deep learning frameworks, or other libraries. |
| Experiment Setup | Yes | The Adam (Kingma & Ba, 2015) optimizer was used with momentums β = (0, 0.999) and constant learning rates (0.0001, 0.0004) for G and D. We did not apply the GAN feature matching loss, and used the VGG perceptual loss only for ablations with λVGG = 10. The coefficient for Label Mix λLM was set to 5 for ADE20k and Cityscapes, and to 10 for COCO-Stuff. All our models use an exponential moving average (EMA) of the generator weights with 0.9999 decay (Brock et al., 2019). All the experiments were run on 4 Tesla V100 GPUs, with a batch size of 20 for Cityscapes, and 32 for ADE20k and COCO-Stuff. The training epochs are 200 on ADE20K and Cityscapes, and 100 for the larger COCO-Stuff dataset. |