Loss Functions
Triplet Loss
Introduced by Dong et al. in Triplet Loss in Siamese Network for Object TrackingThe goal of Triplet loss, in the context of Siamese Networks, is to maximize the joint probability among all score-pairs i.e. the product of all probabilities. By using its negative logarithm, we can get the loss formulation as follows:
$$ L_{t}\left(\mathcal{V}_{p}, \mathcal{V}_{n}\right)=-\frac{1}{M N} \sum_{i}^{M} \sum_{j}^{N} \log \operatorname{prob}\left(v p_{i}, v n_{j}\right) $$
where the balance weight $1/MN$ is used to keep the loss with the same scale for different number of instance sets.
Source: Triplet Loss in Siamese Network for Object Tracking
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Retrieval | 76 | 9.56% |
| Metric Learning | 73 | 9.18% |
| Person Re-Identification | 44 | 5.53% |
| Image Retrieval | 32 | 4.03% |
| Face Recognition | 21 | 2.64% |
| General Classification | 21 | 2.64% |
| Clustering | 18 | 2.26% |
| Image Classification | 17 | 2.14% |
| Few-Shot Learning | 16 | 2.01% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
