Semi-Supervised Learning Methods
Meta Pseudo Labels
Introduced by Pham et al. in Meta Pseudo LabelsMeta Pseudo Labels is a semi-supervised learning method that uses a teacher network to generate pseudo labels on unlabeled data to teach a student network. The teacher receives feedback from the student to inform the teacher to generate better pseudo labels. This feedback signal is used as a reward to train the teacher throughout the course of the student’s learning.
Source: Meta Pseudo Labels
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semi-Supervised Image Classification | 3 | 30.00% |
| Image Classification | 2 | 20.00% |
| Fine-Grained Image Classification | 1 | 10.00% |
| Image Manipulation | 1 | 10.00% |
| Interspecies Facial Keypoint Transfer | 1 | 10.00% |
| Keypoint Detection | 1 | 10.00% |
| Meta-Learning | 1 | 10.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
