Learning To Differentiate using Deep Metric Learning

Recently, machine learning algorithms have contributed greatly to developing very efficient visual search workflows using Convolutional Neural Networks (CNNs). Since data volumes have been increased in recent times, object recognition models are able to recognize the objects and generalize the image features at scale. However, in a challenging classification setting where the number of classes is huge, there are several constraints one needs to address to design effective visual search workflows. To alleviate these issues, deep learning and metric learning collectively form the concept of Deep Metric Learning(DML), also known as Distance Metric Learning. It proposes to train a CNN based nonlinear feature extraction module (or an encoder), that embeds the extracted image features (also called embeddings) that are semantically similar, onto nearby locations while pushing dissimilar image features apart using an appropriate distance metric e.g.