Campus Event Calendar
Event Entry
What and Who
Robust Deep Neural Networks for Computer Vision
Adam Kortylewski
Max-Planck-Institut für Informatik - D6
Joint Lecture Series
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Wednesday, 6 April 2022
12:15
60 Minutes
Virtual talk
Virtual
Saarbrücken
Abstract
Deep learning sparked a tremendous increase in the performance of computer vision systems over the past decade. However, Deep Neural Networks (DNNs) are still far from reaching human-level performance at visual recognition tasks. The most important limitation of DNNs is that they fail to give reliable predictions in unseen or adverse viewing conditions, which would not fool a human observer, such as when objects are partially occluded, seen in an unusual pose or context, or in bad weather. This lack of robustness in DNNs is generally acknowledged, but the problem largely remains unsolved.
In this talk, I will give an overview of the principles underlying my work on building robust deep neural networks for computer vision. My working hypothesis is that vision systems need a causal 3D understanding images by following an analysis-by-synthesis approach. I will discuss a new type of neural network architecture that implements such an approach, and I will show that these generative neural network models are vastly superior to traditional models in terms of robustness, learning efficiency and because they can solve many vision tasks at once. Finally, I will give a brief outlook on current projects of mine and future research directions.
Contact
Jennifer Müller
+49 681 9325 2900
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Virtual Meeting Details
Zoom
997 1565 5535
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Jennifer Müller, 03/31/2022 10:50 -- Created document.