State of the art models for Similarity Learning are all based on Deep Learning architecture using Siamese Network [Gregory et al., 2015]. They define a feature extraction pipeline that creates a latent representation of input data. This embedding …
Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I presented, studied and compared three of the most popular losses for the task of …
In this project, I explored deep similarity learning algorithms and their behaviour with different type of data (sequential data, spatial data, multimodal data). For each of these different modalities, I wrote 2 Medium articles detailing the retained method and providing my implementation.
Deep Similarity Learning is the training of a deep learning architecture to learn to detect similarity and disimilarity between two inputs (or more). In this article, I focused on similarities between sentences, presenting the theory as well as …