Novel Approaches to Similarity Learning

Many fields such as facial verification/identification and recommendation systems utilize similarity learning to achieve their goal. Similarity Learning is mostly applied to images. When we want to compare two images and decide whether they are similar or not, it's best to compare their embeddings produced by a trained CNN. Simply put, embedding is just vectors extracted from the network that contains important patterns and information learned by the network. For example, if we want to compare if a picture of a dog is similar to a cat, we would put the images through the same neural network with the same weights.