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..
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Authors: Hao He, Hao Wang, Guang-He Lee, Yonglong Tian
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evidence on synthetic high-dimensional multi-modal data and image databases (CIFAR-10, STL-10, and Image Net) demonstrates the superiority of our method over both start-of-the-art multi-generator GANs and other probabilistic treatment for GANs. In this section, we evaluate our model with two inference algorithms proposed in Section 3.3 (denoted as Prob GAN-GMA and Prob GAN-PSA). |
| Researcher Affiliation | Academia | Hao He, Hao Wang, Guang-He Lee, Yonglong Tian Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: Our Adapted SGHMC Inference Algorithm |
| Open Source Code | No | We will release our evaluation code soon. |
| Open Datasets | Yes | We evaluate our method on 3 widely-adopted datasets: CIFAR-10 (Krizhevsky et al., 2010), STL-10 (Coates et al., 2011) and Image Net (Deng et al., 2009). |
| Dataset Splits | No | The paper mentions 'CIFAR-10 has 50k training and 10k test' but does not specify details of a validation set split (e.g., percentages, sample counts, or methodology for creating a validation split). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Tensorflow' and 'Py Torch (Paszke et al., 2017)' but does not provide specific version numbers for these or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | For a fair comparison with baselines, we use the same settings as MGAN. We resize the STL-10 and Image Net images down to 48x48 and 32x32 respectively. ... All models are optimized by Adam(Kingma & Ba, 2014) with a learning rate of 2 104. For probabilistic methods, the SGHMC noise factor is set as 3 102. Following the configuration in MGAN, the batch size of generators and discriminators are 120 and 64. |