adambielski/siamese-triplet

Siamese and triplet networks are useful to learn mappings from image to a compact Euclidean space where distances correspond to a measure of similarity [2]. Embeddings trained in such way can be used as features vectors for classification or few-shot learning tasks. Experiments were run in jupyter notebook. We'll go through learning supervised feature embeddings using different loss functions on MNIST dataset. This is just for visualization purposes, thus we'll be using 2-dimensional embeddings which isn't the best choice in practice.