Multidisciplinary Institute in Artificial intelligence

Inherent Interpretability for Deep Learning in Computer Vision

ABSTRACT

Computer Vision has been revolutionized by Machine Learning and in particular Deep Learning. End-to-end trainable models allow to achieve top performance across a wide range of computer vision tasks and settings. While recent progress is remarkable, current deep learning models are hard to interpret. In this talk discuss a new class of neural networks which are performant image classifiers with a high degree of inherent interpretability. In particular, these novel networks perform classification through a series of input-dependent linear transformations, that outperform existing attribution methods both quantitatively as well as qualitatively.

SPEAKER

Bernt Schiele has been Max Planck Director at MPI for Informatics and Professor at Saarland University since 2010.
He studied computer science at the University of Karlsruhe, Germany. He worked on his master thesis in the field of robotics in Grenoble, France, where he also obtained the "diplome d'etudes approfondies d'informatique". In 1994 he worked in the field of multi-modal human-computer interfaces at Carnegie Mellon University, Pittsburgh, PA, USA in the group of Alex Waibel. In 1997 he obtained his PhD from INP Grenoble, France under the supervision of Prof. James L. Crowley in the field of computer vision. The title of his thesis was "Object Recognition using Multidimensional Receptive Field Histograms". Between 1997 and 2000 he was postdoctoral associate and Visiting Assistant Professor with the group of Prof. Alex Pentland at the Media Laboratory of the Massachusetts Institute of Technology, Cambridge, MA, USA. From 1999 until 2004 he was Assistant Professor at the Swiss Federal Institute of Technology in Zurich (ETH Zurich). Between 2004 and 2010 he was Full Professor at the computer science department of TU Darmstadt.

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