Energy-guided Entropic Neural Optimal Transport
Authors: Petr Mokrov, Alexander Korotin, Alexander Kolesov, Nikita Gushchin, Evgeny Burnaev
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In practice, we validate its applicability in toy 2D and image domains. |
| Researcher Affiliation | Academia | 1Skolkovo Institute of Science and Technology, Moscow, Russia 2Artificial Intelligence Research Institute, Moscow, Russia {petr.mokrov,a.korotin}@skoltech.ru |
| Pseudocode | Yes | Algorithm 1: Entropic Optimal Transport via Energy-Based Modelling |
| Open Source Code | Yes | Our code is available at: https: //github.com/Petr Mokrov/Energy-guided-Entropic-OT |
| Open Datasets | Yes | The AFHQ dataset is taken from the Star GAN v2 (Choi et al., 2020) github: https://github.com/clovaai/stargan-v2. For generating Colored MNIST dataset, we make use of the code, provided by the authors of (Gushchin et al., 2023). |
| Dataset Splits | No | The paper mentions drawing samples from source and target distributions (e.g., 'Derive batches {xn}N n=1 = X P, {yn}N n=1 = Y Q of sizes N' in Algorithm 1, and 'For the source and target distributions, we choose P = N(0, ΣX) and Q = N(0, ΣY )' in D.2), but does not explicitly provide specific training, validation, or test dataset split percentages or sample counts. |
| Hardware Specification | Yes | The experiment was conducted on a single GTX 1080 Ti (D.1), The training takes approximately a day on 4 A100 GPUs. (D.3), It takes approximately one day on one V100 GPU (D.4). |
| Software Dependencies | No | The paper states 'Our code is written in Py Torch', but it does not specify version numbers for PyTorch or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | The hyperparameters of Algorithm 1 are as follows: K = 100, σ0 = 1, N = 1024, η = 0.05 for ε = 0.1 and η = 0.005 for ε = 0.001. (D.1) and MLP with Re LU activations and three hidden layers with 1024, 512 and 256 neurons, accordingly. The training hyperparameters are: K = 100, η = 0.008, σ0 = 1.0, N = 128. For both Cat Dog and Wild Dog experiments, we train our model for 11 epochs with a learning rate 10 4 which starts monotonically decreasing to 10 5 after the fifth epoch. (D.3). |