Large Scale Optimal Transport and Mapping Estimation
Authors: Vivien Seguy, Bharath Bhushan Damodaran, Remi Flamary, Nicolas Courty, Antoine Rolet, Mathieu Blondel
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our proposed approach on two applications: domain adaptation and generative modeling. |
| Researcher Affiliation | Collaboration | Vivien Seguy Kyoto University Graduate School of Informatics; Bharath Bhushan Damodaran Universit e de Bretagne Sud IRISA, UMR 6074, CNRS; R emi Flamary Universit e Cˆote d Azur Lagrange, UMR 7293, CNRS, OCA; Nicolas Courty Universit e de Bretagne Sud IRISA, UMR 6074, CNRS; Antoine Rolet Kyoto University Graduate School of Informatics; Mathieu Blondel NTT Communication Science Laboratories |
| Pseudocode | Yes | Algorithm 1 Stochastic OT computation; Algorithm 2 Optimal map learning with SGD |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | We consider the three cross-domain digit image datasets MNIST (Lecun et al., 1998), USPS, and SVHN (Netzer et al., 2011) |
| Dataset Splits | No | The paper specifies dataset sizes for training and target domains but does not explicitly provide percentages or counts for a separate validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Adam optimizer with batch size 1000 is used to optimize the network. The learning rate is varied in {2, 0.9, 0.1, 0.01, 0.001, 0.0001}. The learned Monge map f in Alg. 2 is parameterized as a neural network with two fully-connected hidden layers (d 200 500 d) and Re LU activations, and the weights are optimized using the Adam optimizer with learning rate equal to 10 4 and batch size equal to 1000. For the Sinkhorn algorithm, regularization value is chosen from {0.01, 0.1, 0.5, 0.9, 2.0, 5.0, 10.0}. |