Methods › General › Regularization › Sensor Dropout
Sensor Dropout or SensD
Sensor Dropout
Introduced by Yi Wang et al. in Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
A method that randomly mask out all features coming from a specific sensor in multi-sensor models for Earth observation. Depending on the fusion strategy, the mask out can be done at the input, feature or decision level.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
Increasing the Robustness of Model Predictions to Missing Sensors in Earth Observation 22 Jul 2024 · 2 repositories · arXiv:2407.15512
-
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation 6 Apr 2024 · 0 repositories · arXiv:2404.04629
-
Incomplete Multimodal Learning for Remote Sensing Data Fusion 22 Apr 2023 · 0 repositories · arXiv:2304.11381
-
Self-supervised Vision Transformers for Joint SAR-optical Representation Learning 11 Apr 2022 · 2 repositories · arXiv:2204.05381
Tasks archive 2025-07-28
14 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections