Disentangling by Factorising
Authors: Hyunjik Kim, Andriy Mnih
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Factor VAE to β-VAE on the following data sets with i) known generative factors: 1) 2D Shapes (Matthey et al., 2017): 737,280 binary 64 64 images of 2D shapes with ground truth factors[number of values]: shape[3], scale[6], orientation[40], x-position[32], y-position[32]. 2) 3D Shapes data: 480,000 RGB 64 64 3 images of 3D shapes with ground truth factors: shape[4], scale[8], orientation[15], floor colour[10], wall colour[10], object colour[10] ii) unknown generative factors: 3) 3D Faces (Paysan et al., 2009): 239,840 grey-scale 64 64 images of 3D Faces. 4) 3D Chairs (Aubry et al., 2014): 86,366 RGB 64 64 3 images of chair CAD models. 5) Celeb A (cropped version) (Liu et al., 2015): 202,599 RGB 64 64 3 images of celebrity faces. |
| Researcher Affiliation | Collaboration | 1Deep Mind, UK 2Department of Statistics, University of Oxford. Correspondence to: Hyunjik Kim <hyunjikk@google.com>. |
| Pseudocode | Yes | Algorithm 1 permute dims |
| Open Source Code | No | The paper does not include an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We compare Factor VAE to β-VAE on the following data sets with i) known generative factors: 1) 2D Shapes (Matthey et al., 2017): 737,280 binary 64 64 images of 2D shapes with ground truth factors[number of values]: shape[3], scale[6], orientation[40], x-position[32], y-position[32]. 2) 3D Shapes data: 480,000 RGB 64 64 3 images of 3D shapes with ground truth factors: shape[4], scale[8], orientation[15], floor colour[10], wall colour[10], object colour[10] ii) unknown generative factors: 3) 3D Faces (Paysan et al., 2009): 239,840 grey-scale 64 64 images of 3D Faces. 4) 3D Chairs (Aubry et al., 2014): 86,366 RGB 64 64 3 images of chair CAD models. 5) Celeb A (cropped version) (Liu et al., 2015): 202,599 RGB 64 64 3 images of celebrity faces. |
| Dataset Splits | No | The paper does not explicitly state specific train/validation/test dataset splits (percentages or counts) needed to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, lacking specific details like GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The experimental details such as encoder/decoder architectures and hyperparameter settings are in Appendix A. |