center loss
Type: Concept
As of November 2026, center loss has been mentioned in one podcast episode on Carnegie Mellon University Deep Learning (Jan 2026). Center loss is discussed as a method to improve feature clustering in zero-shot learning by minimizing the distance between samples and their class centers.
| 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 center loss on podcasts?
center 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)