Counterfactuals uncover the modular structure of deep generative models
Authors: Michel Besserve, Arash Mehrjou, Rémy Sun, Bernhard Schölkopf
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS We first investigated modularity of genrative models trained on the Celeb Faces Attributes Dataset (Celeb A)(Liu et al., 2015) and used a basic architecture: a plain β-VAE 2 (Higgins et al., 2017). We ran the full procedure described in Sec. 3, comprised of EIM calculations, clustering of channels into modules, and hybridization of generator samples using these modules. |
| Researcher Affiliation | Academia | Michel Besserve1,2, , Arash Mehrjou1,3, R emy Sun1,3, Bernhard Sch olkopf1 1. MPI for Intelligent Systems, T ubingen, Germany. 2. MPI for Biological Cybernetics, T ubingen, Germany. 3. Department of Computer Science, ETH Z urich, Switzerland. 4. ENS Rennes, France. |
| Pseudocode | No | The paper describes methods textually (e.g., 'the hybridization procedure... goes as follows'), but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Implementations are available on the companion website https://gitlab.tuebingen.mpg.de/besserve/ deepcounterfactuals. |
| Open Datasets | Yes | Celeb Faces Attributes Dataset (Celeb A)(Liu et al., 2015) and Image Net dataset6. http://www.image-net.org/ |
| Dataset Splits | No | The paper mentions using specific datasets like Celeb A and ImageNet, and training models or using pre-trained models, but it does not explicitly provide details on how the datasets were split into training, validation, and test sets for their experiments. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments or training models. |
| Software Dependencies | No | The paper mentions software like 'tensorlayer DCGAN implementation' and 'Tensorflow-hub' for pre-trained models, but it does not specify concrete version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Hyperparameters for both structures are specified in Table 1. Optimization algorithm Adam (β = 0.5) Minimized objective VAE loss (Gaussian posteriors) batch size 64 Beta parameter 0.0005 |