Self-Supervised Learning
NPID
Introduced by Wu et al. in Unsupervised Feature Learning via Non-Parametric Instance DiscriminationNPID (Non-Parametric Instance Discrimination) is a self-supervision approach that takes a non-parametric classification approach. Noise contrastive estimation is used to learn representations. Specifically, distances (similarity) between instances are calculated directly from the features in a non-parametric way.
Source: Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 3 | 15.79% |
| Semi-Supervised Image Classification | 3 | 15.79% |
| Self-Supervised Learning | 2 | 10.53% |
| Self-Supervised Image Classification | 2 | 10.53% |
| Classifier calibration | 1 | 5.26% |
| Few-Shot Learning | 1 | 5.26% |
| Fine-Grained Image Recognition | 1 | 5.26% |
| Image Classification | 1 | 5.26% |
| Semantic Segmentation | 1 | 5.26% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
