Skada Sprint Alert: Contribute to Domain Adaptation in Python

📖 Machine learning models often fail when the data distribution changes between training and testing. That’s where Domain Adaptation comes in — helping models stay reliable across domains.

The benchmark shows deep DA methods struggle beyond computer vision, highlighting their limits on other modalities!

The results show the benefit of DA in some cases but parameter-sensitive shallow methods struggle to adapt to new domains. Better to use low-parameter methods like LinOT & Coral!

This benchmark is done using a realistic scenario comprising the validation of hyperparameters using nested loop and DA scorers!

🔬 What’s inside?
• Multi-Modality Benchmark: 4 simulated + 8 real datasets
• 20 Shallow DA Methods: Reweighting, mapping, subspace alignment & others
• 7 Deep DA Methods: CAN, MCC, MDD, SPA & more
• 7 Unsupervised Validation Scorers

DA adapts machine learning models to distribution shifts between training and test sets. We propose SKADA-Bench, the first comprehensive, reproducible benchmark that evaluates DA methods across multiple modalities: computer vision, natural language processing, tabular, and biomedical data.

🚀 I’m pleased to announce a new preprint!

"SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities"

📢 Check it out & contribute!
📜 Paper: arxiv.org/abs/2407.11676
💻 Code: github.com/scikit-adapt...

📊 Advanced Scorers
- New MixValScorer for mixup validation.
- Enhanced scorer compatibility with deep models.

💡New Deep Domain Adaptation Methods: CAN, SPA, MCC, and MDD.These methods combine the cross entropy loss on the source domain with domain aware losses (graph based, adversarial, class confusion, …).

💡New Shallow Domain Adaptation Methods: MongeAlignment and JCPOT for linear multi-source domain adaptation with optimal transport.

🚀 Skada v0.4.0 is out!

Skada is an open-source Python library built for domain adaptation (DA), helping machine learning models to adapt to distribution shifts.
Github: github.com/scikit-adapt...
Doc: scikit-adaptation.github.io
DOI: doi.org/10.5281/zeno...
Installation: `pip install skada`