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..
Generative Models of Visually Grounded Imagination
Authors: Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki et al. (2017) and the Bi VCCA method of Wang et al. (2016b)) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the Celeb A dataset (Liu et al., 2015). Section 5 reports experimental results on two different datasets. |
| Researcher Affiliation | Collaboration | Ramakrishna Vedantam Georgia Tech EMAIL Ian Fischer Google Inc. EMAIL Jonathan Huang Google Inc. EMAIL Kevin Murphy Google Inc. EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods... by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the Celeb A dataset (Liu et al., 2015). |
| Dataset Splits | Yes | We split the images into a train, val and test set of 85%, 5%, and 10% of the data respectively to create the IID split. We choose the hyperparameters for each method so as to maximize JS-overall, which is an overall measure of correctness and coverage (see Section 3) on a validation set of attribute queries. |
| Hardware Specification | Yes | Our models typically take around a day to train on NVIDIA Titan X GPUs. |
| Software Dependencies | No | The paper mentions software like "Adam" and "DCGAN architecture" but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | We use Adam (Kingma & Ba, 2015) for optimization, with a learning rate of 0.0001, and a minibatch size of 64. We train all models for 250,000 steps (we generally found that the models do not tend to overο¬t in our experiments). We use d = 10 latent dimensions for all models. |