A guest blog post by Thomas Di Martino
This post is part of a series of guest blog posts written by script authors, talking about their entries to the Sentinel Hub Custom Script Contest. Thomas Di Martino and his team (Elise Colin-Koeniguer, Regis Guinvarc’h, and Laetitia Thirion-Lefevre) are among the winners in the third round of the Contest. Their winning script with detailed description is available on our GitHub repository.
Through this article, I will evaluate and compare three different losses for the task of Deep Similarity Learning. If this topic is still not perfectly understandable to you, I have written an article introducing the main concepts with code examples as well as a complete GitHub repository for you to check:
I. Quick overview of the task
II. Siamese Recurrent Network: similarity learning for sequences
III. Losses for Deep Similarity Learning
IV. Concrete Application: question pairs detection
I used for this task the famous Quora question pairs dataset, where the main goal is to predict if two question pairs have…
In this article, I will go through my take on the general concept of Similarity Learning, which processes it involves and how it can be summarized. I will then apply these outlined concepts to the context of sequence similarity detection with question similarities.
When one is doing similarity learning, the same process is always performed:
Presented back in 2017 by the Université de Montpellier, the TiSeLaC challenge  (TiSeLaC for Time Series Land Cover) consists in predicting Land Cover class of pixels in Time Series of satellite images.
Time Series Satellite Imagery is the addition of a temporal dimension to Satellite Imagery. …
As a French PhD student, I am passionate to whatever comes close to Artificial Intelligence and Earth Observation.