19 papers
Private and Stable Test-Time Adaptation with Differential Privacy
Zefeng Li, Qiaoyue Tang, Mathias Lecuyer +1
Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates raise the issue of privacy w.r.…
Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
Nathan Senyard, Salem Hamdani, Astrid Zhang +4
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m spatial resolution. We com…
Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors
Tim G. Zhou, Anthony Fuller, Geoff Pleiss +1
Machine learning models for remote sensing are trained and deployed on a static set of modalities. However, as we equip newer satellites with novel sensors and retire old ones, pra…
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation
Zefeng Li, Evan Shelhamer
Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes. While recent methods have made progress on improving in-d…
Self-Soupervision: Cooking Model Soups without Labels
Anthony Fuller, James R. Green, Evan Shelhamer
Model soups are strange and strangely effective combinations of parameters. They take a model (the stock), fine-tune it into multiple models (the ingredients), and then mix their p…