Summary of Recall and Refine: a Simple but Effective Source-free Open-set Domain Adaptation Framework, by Ismail Nejjar et al.
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
by Ismail Nejjar, Hao Dong, Olga Fink
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel framework for Source-free Open-set Domain Adaptation (SF-OSDA), called Recall and Refine (RRDA). This framework aims to adapt a model from an unlabeled target domain, where novel classes are present. The existing SF-OSDA methods rely on thresholding prediction entropy to identify known or unknown classes, but fail to learn discriminative features for target-private unknown classes. RRDA employs a two-step process: first, it enhances the model’s capacity to recognize unknown classes by training a target classifier with an additional decision boundary guided by synthetic samples generated from target domain features. Then, it adapts the entire model to the target domain, addressing both domain shifts and improving generalization to unknown classes. Any off-the-shelf source-free domain adaptation method can be seamlessly integrated into this framework. The proposed approach is evaluated on three benchmark datasets, demonstrating significant improvements over existing SF-OSDA and OSDA methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in machine learning called Open-set Domain Adaptation (OSDA). This means taking a model that was trained on one type of data and using it to make predictions on another type of data, even if the new data has classes that weren’t seen before. The challenge is that you can’t look at any labeled training data from the old domain. The paper proposes a new way to do this called Recall and Refine (RRDA). It works by first making sure the model can recognize unknown classes, then adapting the whole model to the new data. This helps it make better predictions on the new data. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Machine learning » Recall
