Learning disconnected manifolds: a no GAN’s land
Authors: Ugo Tanielian, Thibaut Issenhuth, Elvis Dohmatob, Jeremie Mary
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the following, we show that our truncation method, JBT, can significantly improve the performances of generative models on several models, metrics and datasets. Furthermore, we compare JBT with over-parametrization techniques specifically designed for disconnected manifold learning. We show that our truncation method reaches or surpasses their performance, while it has the benefit of not modifying the training process of GANs nor using a mixture of generators, which is computationally expensive. Finally, we confirm the efficiency of our method by applying it on top of Big GAN (Brock et al., 2019). |
| Researcher Affiliation | Collaboration | Ugo Tanielian 1 2 Thibaut Issenhuth 2 Elvis Dohmatob 2 Jérémie Mary 2 1Université Paris-Sorbonne, Paris, France 2Criteo AI Lab, France. |
| Pseudocode | No | The paper describes its methods verbally and mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing their source code for the described methodology or a link to a repository. |
| Open Datasets | Yes | We further study JBT on three different datasets: MNIST (Le Cun et al., 1998), Fashion MNIST (Xiao et al., 2017) and CIFAR10 (Krizhevsky et al., 2009). |
| Dataset Splits | No | While the paper mentions using standard datasets, it does not provide specific details on the training, validation, or test split percentages or counts within the provided text. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., exact GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Wasserstein GAN with gradient penalty" and "Big GAN" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In practice, σ in [1e 4; 1e 2] and N = 10 give consistent results. Except for Big GAN, for all our experiments, we use Wasserstein GAN with gradient penalty (Gulrajani et al., 2017), called WGAN for conciseness. |