Siamese Network
Lets first talk about the problem of one shot learning . One shot learning is learning from a single training example. This problem occurs for example in an organization where you want to recognize faces and you might have only one face of the employee. Using a convnet to output a multi-class label is not a great idea as a small training set is not enough to train a classifier and it doesn't scale to new employee joining. Instead, one way to handle this problem is to learn a similarity function between two images. One way to train the neural network to learn the similarity function is via a siamese network. The Siamese network consists of two identical neural network with same parameters so that it computes a distance function between the encodings of the two input images [Ref: DeepFace]. To define an objective function, one way is to use a triplet loss. In a triplet loss, there is an anchor image along with a positive example and a negative example (A, P, N). So what is requir