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..
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Authors: Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, Ahmed Elgammal4836-4843
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct quantitative and qualitative experiments to demonstrate the advantages of our method on several datasets. |
| Researcher Affiliation | Academia | Department of Computer Science Rutgers University EMAIL, EMAIL |
| Pseudocode | No | The paper includes diagrams of model structures but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All the code to reproduce our experiments is available on Git Hub, and training configurations can be found there. |
| Open Datasets | Yes | quantitative experiments on the d Sprites datasets (Matthey et al. 2017) following the metric proposed by Kim and Mnih (2018). After that, we show the superiority of OOGAN in generating high-quality images while maintaining competitive disentanglement compared to VAE-based models on Celeb A (Liu et al. 2015) and 3D-chair (Aubry etal. 2014) data. |
| Dataset Splits | No | The paper uses standard datasets and refers to setups from prior work (Kim and Mnih 2018; Jeon, Lee, and Kim 2019), but it does not explicitly state the specific training, validation, and test split percentages or sample counts within its own text. |
| Hardware Specification | Yes | We perform all the experiments on one NVIDIA RTX 2080Ti GPU |
| Software Dependencies | No | The paper states that code is available on GitHub with training configurations but does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | On the 3D Chairs data, we use 64x64 RGB images with batch size 64 for all training runs. and where varying the hyperparameters of λ (1 to 5) and γ (0.2 to 2) in our loss function always yields consistent performance. |