Hi there, I’m Robert!
I’m a Research Scientist at Google Brain, located in Toronto.
During my PhD at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and the University of Tübingen, I was fortunate to work with Felix Wichmann, Matthias Bethge and Wieland Brendel.
Why do Deep Neural Networks see the world as they do?
I’m interested in the fascinating area that lies at the intersection of Deep Learning and Visual Perception.
I want to understand why Deep Neural Networks (DNNs) see the world as they do. Visual perception is a process of inferring—typically reasonably accurate—hypotheses about the world. But what are the hypotheses and assumptions that DNNs make? Answering this question involves understanding the limits of their abilities (when do machines fail, and why?), the biases that they incorporate (e.g. texture bias, a reliance on local features) and the underlying pattern behind some of their successes (such as shortcut learning, or “cheating”).
When comparing DNNs to human perception, I develop quantitative methods to identify areas where DNNs are still falling short of the remarkably robust, flexible and general representations of the human visual system and in a second step seek to overcome these differences. Ultimately, I am convinced that understanding why DNNs see the world as they do holds the key towards making them more interpretable, robust and reliable: Once we have understood DNNs, we can build DNNs that truly “understand”.
- November 2022: Our paper “Beyond neural scaling laws: beating power law scaling via data pruning” has received an Outstanding Paper Award at NeurIPS 2022!
- July 2022: My dissertation “To err is human? A functional comparison of human and machine decision-making” has been recognized with the University of Tübingen’s 2022 dissertation award!
- February 2022: “The bittersweet lesson: data-rich models narrow the behavioural gap to human vision” is accepted as a talk at VSS 2022 in Florida!
Click here for more news.