CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Authors: Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider the application of conditional and interventional sampling of face images with binary feature labels, such as mustache, young. We evaluate our Ci GM training framework on the labeled Celeb A data (Liu et al. (2015)). We empirically show that Causal GAN and Causal BEGAN can produce label-consistent images even for label combinations realized under interventions that never occur during training, e.g., "woman with mustache"2. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering The University of Texas at Austin Austin, TX, USA mkocaoglu@utexas.edu,22csnyder@gmail.com, dimakis@austin.utexas.edu,sriram@austin.utexas.edu |
| Pseudocode | No | The paper describes model architectures and training procedures in text and mathematical formulas, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an unambiguous statement or a direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our Ci GM training framework on the labeled Celeb A data (Liu et al. (2015)). |
| Dataset Splits | No | The paper mentions using the Celeb A dataset but does not explicitly provide details about training, validation, and test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using DCGAN and Wasserstein GAN but does not provide specific software dependency details such as library names with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | For the training, we used 25 Wasserstein discriminator (critic) updates per generator update, with a learning rate of 0.0008. Compared to DCGAN an important distinction is that we make 6 generator updates for each discriminator update on average. We use the same learning rate (0.00008) for both the generator and discriminator and do 1 update of each simultaneously (calculating the for each before applying either). We simply use γ1 = γ2 = γ3 = 0.5. We do use customized margin learning rates λ1 = 0.001, λ2 = 0.00008, λ3 = 0.01 |