Regularized Wasserstein Means for Aligning Distributional Data
Authors: Liang Mi, Wen Zhang, Yalin Wang5166-5173
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout. We evaluate our method on the office-31 dataset (Saenko and others 2010). Table 1: Classification Accuracy (%) on Office-31 W A |
| Researcher Affiliation | Academia | Liang Mi, Wen Zhang, Yalin Wang Arizona State University {liangmi, wzhan139, ylwang}@asu.edu |
| Pseudocode | Yes | Algorithm 1: Wasserstein Means Algorithm 2: Regularized Wasserstein Means |
| Open Source Code | Yes | Code is available at https://github.com/icemiliang/pyvot |
| Open Datasets | Yes | We evaluate our method on the office-31 dataset (Saenko and others 2010). |
| Dataset Splits | No | The paper describes data selection (e.g., 'randomly select 20 samples per class from Amazon and 10 samples per class from Webcam') but does not specify explicit train/validation/test splits or cross-validation setup. |
| Hardware Specification | Yes | CPU: Intel i5-7640x 4.0 GHz. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | Yes | The regularization weight of OTDA Laplacian is 0.3. It is from a search in {1, 0.3, 0.1, 0.03, 0.01}. The weight of RWM is 1 from a search in {3, 1, 0.3, 0.1, 0.03, 0.01} |