triplet loss
Type: Concept
As of November 2026, triplet loss has been mentioned in one podcast episode on Carnegie Mellon University Deep Learning (Jan 2026). Triplet loss is explained as a contrastive loss that enforces relative distances between samples to improve feature separation in zero-shot learning.
| Mentioned in | 1 episode on 1 podcast (as of November 2026) |
|---|---|
| First heard | Jan 2026 on Carnegie Mellon University Deep Learning |
| Field | Machine Learning |
Who talks about triplet loss on podcasts?
triplet loss came up; the most recent mention was Jan 2026 on Carnegie Mellon University Deep Learning.
- 0.20 Losses part1 on Carnegie Mellon University Deep Learning (Jan 2026)