Summary of Domain Agnostic Conditional Invariant Predictions For Domain Generalization, by Zongbin Wang et al.
Domain Agnostic Conditional Invariant Predictions for Domain Generalization
by Zongbin Wang, Bin Pan, Zhenwei Shi
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| 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 Discriminant Risk Minimization (DRM) theory and algorithm aim to develop a model that can perform well on unseen target domains without requiring domain labels. By reducing the discrepancy of prediction distributions between overall source domains and subsets, the DRM theory proves that invariant features can be captured. The algorithm consists of Bayesian inference and a new penalty term called Categorical Discriminant Risk (CDR). Empirical support is provided by evaluating the algorithm against various domain generalization methods on multiple real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A model that can work well in different environments without needing special labels for each one sounds like a great idea! The problem is, most models need these labels to learn. The researchers came up with a new way of thinking called Discriminant Risk Minimization (DRM) that doesn’t require labels. They show that if you make the predictions from all the training data look similar, you can find features that are the same everywhere. To do this, they created an algorithm that combines two parts: one that makes the model’s output a probability distribution and another part that helps the model find these invariant features. This new approach was tested on many real-world datasets and worked well. |
Keywords
» Artificial intelligence » Bayesian inference » Domain generalization » Probability
