Wasserstein-2 Generative Networks
Authors: Alexander Korotin, Vage Egiazarian, Arip Asadulaev, Alexander Safin, Evgeny Burnaev
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From the practical side, we evaluate our algorithm on a wide range of tasks: image-to-image color transfer, latent space optimal transport, image-to-image style transfer, and domain adaptation. In this section, we experimentally evaluate the proposed model. In Subsection 5.1, we apply our method to estimate optimal transport maps in the Gaussian setting. In Subsection 5.2, we consider latent space mass transport. In Subsection 5.3, we experiment with image-to-image style translation. |
| Researcher Affiliation | Academia | Alexander Korotin Skolkovo Institute of Science and Technology Moscow, Russia a.korotin@skoltech.ru; Vage Egiazarian Skolkovo Institute of Science and Technology Moscow, Russia vage.egiazarian@skoltech.ru; Arip Asadulaev ITMO University Saint Petersburg, Russia aripasadulaev@itmo.ru; Aleksandr Safin Skolkovo Institute of Science and Technology Moscow, Russia aleksandr.safin@skoltech.ru; Evgeny Burnaev Skolkovo Institute of Science and Technology Moscow, Russia e.burnaev@skoltech.ru |
| Pseudocode | Yes | Algorithm 1: Numerical Procedure for Optimizing Regularized Correlations (12) |
| Open Source Code | Yes | The code is written on Py Torch framework and is publicly available at https://github.com/iamalexkorotin/Wasserstein2Generative Networks. |
| Open Datasets | Yes | We test our algorithm on Celeb A image generation (64 64). We test our model on MNIST ( 60000 images; 28 28) and USPS ( 10000 images; rescaled to 28 26) digits datasets. We experiment with Conv ICNN potentials on publicly availaible Winter2Summer and Photo2Cezanne datasets containing 256 256 pixel images. |
| Dataset Splits | No | No explicit information on training/validation/test splits (e.g., percentages, sample counts, or clear predefined split references for validation) was found in the text for general dataset usage. |
| Hardware Specification | Yes | The networks are trained on a single GTX 1080Ti. |
| Software Dependencies | No | The code is written on Py Torch framework. |
| Experiment Setup | Yes | For each particular problem the networks are trained for 30000 iterations with 1024 samples in a mini batch. Adam optimizer Kingma & Ba (2014) with lr = 10 3 is used. We put λ = 1 in our cycle regularization and impose additional 10 10 L1 regularization on the weights. Adam optimizer with lr = 3 10 4 is used. We put λ = 100 as the cycle regularization parameter. |