The paper introduces the DRIFT dataset, a large-scale dataset across five European countries for domain-adaptive regression on forest monitoring tasks. The dataset includes aerial and satellite imagery with annotations for canopy height, tree count, and tree cover fraction.
The authors propose a framework for universal, source-free domain adaptation, where a model is trained on the source domain without any prior knowledge about the target domain, and then adapted to the target domain using a few labeled examples.
The key insight is that enforcing an ordered embedding space in the source domain, using geometric order learning (GOL), enables effective cross-domain regression. The ordered embeddings generalize better across domains compared to direct regression. The authors further propose Manifold Diffusion for Regression (MDR), a transductive method that leverages the structure of the embedding space to refine predictions on the target domain.
The experiments show that the proposed GOL+MDR approach outperforms inductive baselines, especially when the domain gap between source and target is large. Transductive methods like MDR are able to better capture the structure of the target domain embeddings and make more accurate predictions. The authors also find that the performance of direct cross-domain regression can serve as an indicator of the domain gap.
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by Sizhuo Li,Di... klo arxiv.org 05-02-2024
https://arxiv.org/pdf/2405.00514.pdfSyvällisempiä Kysymyksiä