LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Authors: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 jw2yang@vt.edu Anitha Kannan Facebook AI Research Menlo Park, CA akannan@fb.com Dhruv Batra and Devi Parikh Georgia Institute of Technology Atlanta, GA {dbatra, parikh}@gatech.edu |
| 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. |