Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
You Only Need Adversarial Supervision for Semantic Image Synthesis
Authors: Edgar Schönfeld, Vadim Sushko, Dan Zhang, Juergen Gall, Bernt Schiele, Anna Khoreva
ICLR 2021 | Venue PDF | 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. |