Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
Authors: Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao, Junya Honda, Issei Sato, Masashi Sugiyama4122-4129
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results demonstrate the advantages of S-disc over the existing discrepancy measures. [...] We demonstrate the effectiveness of S-disc for unsupervised domain adaptation in experiments. |
| Researcher Affiliation | Collaboration | The University of Tokyo, Tokyo, Japan RIKEN, Tokyo, Japan |
| Pseudocode | Yes | Algorithm 1: S-disc Estimation for the 0-1 Loss |
| Open Source Code | No | The paper cites "ar Xiv:1809.03839" as a longer version of the paper, but does not provide a direct link to source code for the described methodology. It mentions using "scikit-learn" and "picos/cvxopt" as third-party tools but not its own implementation code. |
| Open Datasets | Yes | Here, we used MNIST (Le Cun, Cortes, and Burges 2010) dataset [...] Clean source domains: Five grayscale MNIST-M (Ganin et al. 2016), [...] Target domain: MNIST. |
| Dataset Splits | No | The paper specifies the total number of examples used for source and target domains (e.g., 'The number of examples was ranged from {1000, 2000, ..., 20000} for each domain'), but it does not provide explicit train/validation/test split percentages, absolute counts for each split, or references to predefined splits for reproduction. |
| Hardware Specification | Yes | The simulation was run on 2.8GHz Intel R Core i7. |
| Software Dependencies | No | The paper mentions using 'scikit-learn (Pedregosa et al. 2011)', 'picos' and 'cvxopt', but it does not specify version numbers for these software dependencies, which are crucial for reproducibility. |
| Experiment Setup | Yes | logistic regression implemented with scikit-learn with default parameters (Pedregosa et al. 2011). [...] The Gaussian noise with standard deviation ϵ = 30, 40, 50 were added and clipped to force the value to between 0 255. |