Semi-Supervised Learning Methods
FixMatch
Introduced by Sohn et al. in FixMatch: Simplifying Semi-Supervised Learning with Consistency and ConfidenceFixMatch is an algorithm that first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images. For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction. The model is then trained to predict the pseudo-label when fed a strongly-augmented version of the same image.
Description from: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Image credit: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Source: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Pseudo Label | 18 | 15.65% |
| Semi-Supervised Image Classification | 16 | 13.91% |
| Image Classification | 8 | 6.96% |
| Semantic Segmentation | 7 | 6.09% |
| Semi-Supervised Semantic Segmentation | 5 | 4.35% |
| Domain Adaptation | 3 | 2.61% |
| Classification | 3 | 2.61% |
| Unsupervised Domain Adaptation | 2 | 1.74% |
| Image Segmentation | 2 | 1.74% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
