Summary of Continual Test-time Domain Adaptation Via Dynamic Sample Selection, by Yanshuo Wang et al.
Continual Test-time Domain Adaptation via Dynamic Sample Selection
by Yanshuo Wang, Jie Hong, Ali Cheraghian, Shafin Rahman, David Ahmedt-Aristizabal, Lars Petersson, Mehrtash Harandi
First submitted to arxiv on: 5 Oct 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper proposes a novel method called Dynamic Sample Selection (DSS) for Continual Test-time Domain Adaptation (CTDA), which enables pre-trained models to adapt gradually to a sequence of target domains without accessing the source data. The DSS method consists of dynamic thresholding, positive learning, and negative learning processes that selectively update model parameters based on the quality of pseudo-labels generated from unlabeled unknown environment data. By dynamically identifying and filtering out noisy predictions, the proposed approach demonstrates improved performance in image and 3D point cloud domains compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps AI models learn new things without needing all their old training data. It’s like a superpower that lets them quickly adapt to new situations! The researchers came up with a way called Dynamic Sample Selection (DSS) that makes the model better at learning from uncertain information. They tested it and found it worked really well in both 2D pictures and 3D point clouds. This means AI can be used in many more areas than before, like image recognition and robotics. |
Keywords
* Artificial intelligence * Domain adaptation
