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..
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Authors: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct qualitative and quantitative evaluations on three datasets: 1) MNIST (Le Cun et al., 1998); 2) CIFAR-10 (Krizhevsky & Hinton, 2009); 3) CUB-200 (Welinder et al., 2010). |
| Researcher Affiliation | Collaboration | Jianwei Yang Virginia Tech Blacksburg, VA EMAIL Anitha Kannan Facebook AI Research Menlo Park, CA EMAIL Dhruv Batra and Devi Parikh Georgia Institute of Technology Atlanta, GA EMAIL |
| Pseudocode | Yes | Pseudo-code for our approach and detailed model configuration are provided in the Appendix. |
| Open Source Code | No | The paper states "We develop LR-GAN based on open source code1. 1https://github.com/soumith/dcgan.torch", which refers to a third-party baseline (DCGAN) used for development, not the specific source code for the LR-GAN methodology itself. |
| Open Datasets | Yes | We mainly evaluate our approach on four datasets: MNIST-ONE (one digit) and MNIST-TWO (two digits) synthesized from MNIST (Le Cun et al., 1998), CIFAR-10 (Krizhevsky & Hinton, 2009) and CUB-200 (Welinder et al., 2010). |
| Dataset Splits | No | The paper mentions training and testing, and refers to a 'validation set' in the context of evaluation metrics, but does not explicitly provide specific details of training/validation/test dataset splits (e.g., percentages or exact counts) for their own experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper states 'We develop LR-GAN based on open source code1. 1https://github.com/soumith/dcgan.torch', implying the use of Torch, but it does not specify version numbers for Torch or any other software dependencies. |
| Experiment Setup | Yes | The dimensions of random vectors and hidden vectors are all set to 100. In both generator and discriminator, all the (fractional) convolutional layers have 4 4 filter size with stride 2. Please see the Appendix (Section 6.2) for details about the configurations. |