Mapping Estimation for Discrete Optimal Transport
Authors: Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing. The paper includes a dedicated section '4 Experiments' with detailed tables (Table 1 and Table 2) showing accuracy results on Moons and Office-Caltech datasets, and Figure 2 illustrating image editing results, all of which are empirical evaluations. |
| Researcher Affiliation | Academia | Micha el Perrot Univ Lyon, UJM-Saint-Etienne, CNRS, Lab. Hubert Curien UMR 5516, F-42023 michael.perrot@univ-st-etienne.fr Nicolas Courty Universit e de Bretagne Sud, IRISA, UMR 6074, CNRS, courty@univ-ubs.fr R emi Flamary Universit e Cˆote d Azur, Lagrange, UMR 7293 , CNRS, OCA remi.flamary@unice.fr Amaury Habrard Univ Lyon, UJM-Saint-Etienne, CNRS, Lab. Hubert Curien UMR 5516, F-42023 amaury.habrard@univ-st-etienne.fr |
| Pseudocode | Yes | Algorithm 1: Joint Learning of L and γ. input :Xs, Xt source and target examples and λγ, λT hyper parameters. output:L, γ. 2 Initialize k = 0, γ0 Π and L0 = I 4 Learn γk+1 solving problem (6) with fixed Lk using a Frank-Wolfe approach. 5 Learn Lk+1 using Equation (9), (12) or their biased counterparts with fixed γk+1. 6 Set k = k + 1. 7 until convergence |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for their proposed methodology. |
| Open Datasets | Yes | We consider two domain adaptation (DA) datasets, namely Moons [21] and Office Caltech [22]. These datasets are standard and cited with their respective papers [21] and [22], confirming their public availability. |
| Dataset Splits | Yes | For the Moons dataset, 'we consider 300 source and target examples for training'. For the Office-Caltech dataset, 'During the training process we consider all the examples from the source domain and half of the examples from the target domain'. The paper also states: 'All the hyper-parameters are tuned according to a grid search on the source and target training instances using a circular validation procedure derived from [21, 25] and described in the supplementary material.' |
| Hardware Specification | No | The paper states that 'each example is computed in less than 30s on a standard personal laptop.' This description lacks specific details such as CPU, GPU, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or their version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | For GFK and SA we choose the dimension of the subspace d {3, 6, . . . , 30}, for L1L2 and OTE we set the parameter for entropy regularization in {10 6, 10 5, . . . , 105}, for L1L2 we choose the class related parameter η {10 5, 10 4, . . . , 102}, for all our methods we choose λT , λγ {10 3, 10 2, . . . , 100}. For the image editing task, specific λT and λγ values are provided: '(λT = 10 2, λT = 103 for respectively the linear and kernel versions, and λγ = 10 7 for both cases).' |