Methods › Computer Vision › Image Data Augmentation › Object Dropout
Object Dropout
Introduced by Arjit Jain et al. in Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Object Dropout is a technique that perturbs object features in an image for noisy student training. It performs at par with standard data augmentation techniques while being significantly faster than the latter to implement.
Papers archive 2025-07-28
1 shown of 1, 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.
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Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning 19 Aug 2021 · 1 repository
Tasks archive 2025-07-28
5 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 |
|---|---|
| Data Augmentation | 1 |
| Image Augmentation | 1 |
| Image Captioning | 1 |
| Semi Supervised Learning for Image Captioning | 1 |
| image-classification | 1 |
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