Summary of Layer-wise Model Merging For Unsupervised Domain Adaptation in Segmentation Tasks, by Roberto Alcover-couso et al.
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks
by Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo, Jose M Martínez
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 approach introduces a cost-free method for model merging in the context of Unsupervised Domain Adaptation (UDA) for Semantic and Panoptic Segmentation. By layer-wise integrating merged models, the approach maintains the distinctiveness of task-specific final layers while unifying initial layers associated with feature extraction. This ensures parameter consistency across all layers, boosting performance. The method is applied to different architectures and datasets, demonstrating substantial UDA improvements without additional costs for merging same-architecture models from distinct datasets or different-architecture models with a shared backbone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces an easy way to combine the knowledge of multiple trained models without spending extra resources. It works by combining the early layers of these models while keeping the final layers separate, which helps improve performance in tasks like image segmentation. The approach is tested on several datasets and architectures, showing significant improvements for both same-architecture models from different datasets and different-architecture models with a shared backbone. |
Keywords
» Artificial intelligence » Boosting » Domain adaptation » Feature extraction » Image segmentation » Unsupervised
