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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Authors: Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift. and 4. Experiments Datasets. We evaluate on Office-31, Office-Home and Vis DA2017. |
| Researcher Affiliation | Collaboration | 1Imagia, Canada 2Dalhousie University, Canada 3Mila, Universit e de Montr eal, Canada 4Polish Academy of Sciences, Poland. |
| Pseudocode | Yes | Algorithm 1 The proposed implicit alignment training |
| Open Source Code | Yes | Code: https://github.com/xiangdal/implicit_alignment |
| Open Datasets | Yes | Datasets. We evaluate on Office-31 (Saenko et al., 2010), Office-Home (Venkateswara et al., 2017) and Vis DA2017 (synthetic real) (Peng et al., 2017) |
| Dataset Splits | No | The paper uses well-known datasets but does not explicitly provide specific training, validation, or test dataset split percentages or counts for its experiments. |
| Hardware Specification | Yes | Xiang Jiang acknowledges the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research. |
| Software Dependencies | No | The paper mentions deep learning models and datasets but does not provide specific version numbers for software dependencies like PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | The batch size is 31 for Office-31 and 50 for Office-Home. and We only update pseudo-labels periodically, i.e., every 20 steps, instead of at every training step. |