LoRA, a parameter-efficient fine-tuning method, performs on par with full fine-tuning for the largest geospatial foundation model tested, while saving memory, but the paper's broad claims about PEFT are only partly supported by its data.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Work- shops
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Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models
LoRA, a parameter-efficient fine-tuning method, performs on par with full fine-tuning for the largest geospatial foundation model tested, while saving memory, but the paper's broad claims about PEFT are only partly supported by its data.