Explainable AI for Nature Conservation
Diego Marcos (PostDoc)
As Deep Learning gets better at visual tasks, including species identification, the learned reasoning behind its decisions gets increasingly obscure. This is in contrast with the procedures developed by taxonomists, the experts in charge of defining the hierarchy of natural species, for manual species recognition. These procedures lead users to follow an identification key, a structured set of attribute observations, to reach a final conclusion. I will be working towards incorporating this structured reasoning into Deep Learning models for species recognition such that their results become more interpretable, hopefully helping experts to spot mistakes or even yet-to-be-described species, and offering amateur users an expert explanation that can help them become experts themselves.
|Primary Host:||Devis Tuia (EPFL)|
|Exchange Host:||Zeynep Akata (University of Tübingen)|
|PostDoc Duration:||01 February 2019 - 31 January 2023|
|Exchange Duration:||01 August 2020 - 31 October 2020 - Ongoing|