Explaining and Representing Novel Concepts With Minimal Supervision - VVSOR - VVSOR

Vereniging voor Statistiek en Operations Research
30 November 2018

Explaining and Representing Novel Concepts With Minimal Supervision

We cordially invite you to the next meeting of the Thematic Statistics Seminar with current focus on Machine Learning from a statistical perspective on Friday, November 30 in Leiden:

Speaker: Zeynep Akata (University of Amsterdam)
Title: Explaining and Representing Novel Concepts With Minimal Supervision
Time: 15:00-16:00, November 30, 2018
Location: Room 403, Snellius Building, Leiden University, Niels Bohrweg 1, Leiden

 

Abstract:
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image properties which justify visual predictions. In this talk, I will present my past and current work on Zero-Shot Learning, Vision and Language for Generative Modeling and Explainable Artificial Intelligence where we show (1) how to generalize image classification models to cases when no visual training data is available, (2) how to generate images andimage features using detailed visual descriptions, and (3) how our models focus on discriminating properties of the visible object, jointly predict a class label, explain why/not the predicted label is chosen for the image.

For the list of upcoming talks and further information about the seminar please visit the seminar webpage: https://mschauer. github.io/StructuresSeminar/