Summary of Gradient-based Class Weighting For Unsupervised Domain Adaptation in Dense Prediction Visual Tasks, by Roberto Alcover-couso et al.
Gradient-based Class Weighting for Unsupervised Domain Adaptation in Dense Prediction Visual Tasks
by Roberto Alcover-Couso, Marcos Escudero-Viñolo, Juan C. SanMiguel, Jesus Bescós
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper tackles the challenge of class imbalance in unsupervised domain adaptation for visual tasks like semantic and panoptic segmentation. Existing methods often experience performance degradation when dealing with highly imbalanced dense prediction tasks. The issue lies in the lack of equivalent priors between source and target domains, making class-imbalance techniques used for other areas ineffective. The paper proposes a Gradient-based class weighting (GBW) learning method that dynamically estimates class-weights through loss gradient, increasing the contribution of classes hindered by large-represented ones. GBW adapts to iteration training outcomes without explicitly curricular learning patterns. The approach is validated across architectures, UDA strategies, tasks, and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in computer vision where images from different sources have different levels of information. It helps machines learn better by giving more importance to classes that are not well-represented. This makes the machine learn better and more accurate. The method uses the idea that the loss function can be changed to give more weight to these underrepresented classes. This approach is tested on various datasets and architectures, showing good results. |
Keywords
» Artificial intelligence » Domain adaptation » Loss function » Unsupervised
