The 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.

**Inception v2** is the second generation of Inception convolutional neural network architectures which notably uses [batch normalization](https://paperswithcode.com/method/batch-normalization). Other changes include dropping [dropout](https://paperswithcode.com/method/dropout) and removing [local response normalization](https://paperswithcode.com/method/local-response-normalization), due to the benefits of batch normalization.

Inception v2

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Triplet Loss

Triplet Loss in Siamese Network for Object Tracking

**NeuroTactic** is a model for theorem proving which leverages [graph neural networks](https://paperswithcode.com/methods/category/graph-models) to represent the theorem and premises, and applies graph contrastive learning for pre-training. Specifically, premise selection is designed as a pretext task for the graph contrastive learning approach. The learned representations are then used for the downstream task, tactic prediction

Source	Triplet Loss in Siamese Network for Object Tracking
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com