Summary of Skada-bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation on Diverse Modalities, by Yanis Lalou et al.
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
by Yanis Lalou, Théo Gnassounou, Antoine Collas, Antoine de Mathelin, Oleksii Kachaiev, Ambroise Odonnat, Alexandre Gramfort, Thomas Moreau, Rémi Flamary
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, SKADA-bench, evaluates unsupervised domain adaptation (DA) methods on various modalities beyond computer vision tasks. The framework addresses methodological difficulties in selecting hyperparameters by using nested cross-validation and unsupervised model selection scores. Existing shallow algorithms are evaluated on simulated and real-world datasets across diverse modalities like images, text, biomedical, and tabular data. The benchmark provides practical guidance for real-life applications and highlights the importance of realistic validation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SKADA-bench is a new way to test how well machine learning models work when they’re adapted from one kind of data to another without being trained on that second type of data. This helps us figure out which model choices are best and gives useful tips for using these models in real life. |
Keywords
» Artificial intelligence » Domain adaptation » Machine learning » Unsupervised
