Methods › General › Regularization › Temporal Dropout

Temporal Dropout or TempD

Temporal Dropout

5 papers tagged archive 2025-07-28

Introduced by Nando Metzger et al. in Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

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 time-step in time-series data. If the model used is independent of uneven sequences or missing data in time-series, like attention-based transformers, the masked time-steps can be just ignored in the forward prediction of the model. Otherwise, they have to be masked out with some numerical value.

Paper Source

Papers archive 2025-07-28

5 shown of 5, 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
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
27 Apr 2023 · 1 repository · arXiv:2304.14065

Syntology ran 0 of 13 samples · 13 unverified
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
14 Dec 2021 · 1 repository · arXiv:2112.07558
Fine-tuning Handwriting Recognition systems with Temporal Dropout
31 Jan 2021 · 1 repository · arXiv:2102.00511
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
4 Dec 2020 · 1 repository · arXiv:2012.02542

Tasks archive 2025-07-28

12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Crop Classification	3
Earth Observation	2
Time Series Analysis	2
Air Quality Inference	1
General Classification	1
Handwriting Recognition	1
MULTI-VIEW LEARNING	1
Panoptic Segmentation	1
Self-Supervised Learning	1
Semantic Segmentation	1
Time Series	1
Transfer Learning	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

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

Regularization

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