Methods › Computer Vision › Generative Training › Informative Sample Mining Network
Informative Sample Mining Network
Introduced by Jie Cao et al. in Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Informative Sample Mining Network is a multi-stage sample training scheme for GANs to reduce sample hardness while preserving sample informativeness. Adversarial Importance Weighting is proposed to select informative samples and assign them greater weight. The authors also propose Multi-hop Sample Training to avoid the potential problems in model training caused by sample mining. Based on the principle of divide-and-conquer, the authors produce target images by multiple hops, which means the image translation is decomposed into several separated steps.
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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Informative Sample Mining Network for Multi-Domain Image-to-Image Translation 5 Jan 2020 · 0 repositories · arXiv:2001.01173
Tasks archive 2025-07-28
3 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 |
|---|---|
| Image-to-Image Translation | 1 |
| Informativeness | 1 |
| Translation | 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