![]() ![]() I will start by introducing the idea and framework of Deep Transformation Invariant image analysis in the case of image clustering, where I will show that a simple modification of the standard K-means algorithm can lead to state of the art image clustering, while computing distances in pixel space and being easy to interpret. I will present our recent work on analyzing the content of image collections by learning a simple prototype-based model of images. In 2015, he spent a year working as a postdoc with Alexei Efros in UC Berkeley.Īnalysis by synthesis for interpretable object discovery ![]() His PhD on 3D shapes representations at ENS was co-advised by Josef Sivic (INRIA) and Daniel Cremers (TUM). His work is mainly focussed on Computer Vision and Deep Learning, and their intersection with Computer Graphics, Machine Learning, and Digital Humanities. Mathieu Aubry is a tenured researcher in the Imagine team of Ecole des Ponts. Josef Sivic, Czech Technical University in Prague and Inria. ![]() Beomjoon Kim, Korea Advanced Institute of Science and Technology (KAIST).Joohyung Kim, University of Illinois Urbana-Champaign.Jemin Hwangbo, Korea Advanced Institute of Science and Technology (KAIST).Martial Hebert, Carnegie Mellon University.Hatice Gunes, University of Cambridge, UK.Angela Dai, Technical University of Munich.Sungjoon Choi, Department of AI, Korea University.Andrea Cavallaro, Queen Mary University of London.Joschka Boedecker, Albert-Ludwigs-Universität Freiburg.Mathieu Aubry, École des Ponts ParisTech. ![]()
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