In the Andrew-NG coursera course on Convnets he talked about triplet loss function for one shot face recognition.
The formula given in the video is, $$\to \small \small \small ||f(A)-f(P)||^2 \;+\;\alpha \leq\;||f(A)-f(N)||^2$$ $$\to \small \small \small D(A, P) + \alpha \leq D(A, N)$$ $$L(A,P,N) = max(||f(A)-f(P)||^2 - ||f(A)-f(N)||^2 + \alpha, 0)$$ Here, $$f(A) - \small \text{ Person }A$$ $$f(P) - \small \text{Different Picture of Person }A$$ $$F(N) - \small \text{Another Person}$$
I couldn't understand why did we use $\alpha$ in the formula. I understood that the ideal loss function is to decrease $\small \small D(A, P)$ and increase $\small \small D(A, N)$ but if we add $\alpha$ to $\small \small D(A,P)$ it will increase it which is not we require right?