Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Federated Adversarial Domain Adaptation
Authors: Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we perform extensive experiments on several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting. |
| Researcher Affiliation | Academia | Xingchao Peng Boston University Boston, MA 02215, USA EMAIL Zijun Huang Columbia University New York City, NY 10027, USA EMAIL Yizhe Zhu Rutgers University Piscataway, NJ 08854, USA EMAIL Kate Saenko Boston University Boston, MA 02215, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Federated Adversarial Domain Adaptation |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | We test our model on the following tasks: digit classification (Digit-Five), object recognition (Office Caltech10 (Gong et al., 2012), Domain Net (Peng et al., 2018)) and sentiment analysis (Amazon Review dataset (Blitzer et al., 2007a)). |
| Dataset Splits | Yes | Table 10: Detailed number of samples we used in our experiments. Digit-Five Splits mnist mnist_m svhn syn usps Total Train 25,000 25,000 25,000 25,000 7,348 107,348 Test 9,000 9,000 9,000 9,000 1,860 37,860 [...] Domain Net Splits clp inf pnt qdr rel skt Total Train 34,019 37,087 52,867 120,750 122,563 49,115 416,401 Test 14,818 16,114 22,892 51,750 52,764 21,271 179,609 |
| Hardware Specification | Yes | We perform our experiments on a 10 Titan-Xp GPU cluster and simulate the federated system on a single machine (as the data communication is not the main focus of this paper). |
| Software Dependencies | No | Our model is implemented with Py Torch. However, no specific version number for PyTorch or any other software dependency is provided. |
| Experiment Setup | Yes | The detailed architecture of our model can be found in Table 7 (see supplementary material). ... Details of our model are listed in Table 9 (supplementary material). |