Generative Models of Visually Grounded Imagination
Authors: Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | 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 vrama@gatech.edu Ian Fischer Google Inc. iansf@google.com Jonathan Huang Google Inc. jonathanhuang@google.com Kevin Murphy Google Inc. kpmurphy@google.com |
| 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 overfit in our experiments). We use d = 10 latent dimensions for all models. |