[Lisa_seminaires] Timnit Gebru (Stanford CV Lab, PhD candidate), May 5, 13:45, AA5340

Junyoung Chung elecegg at gmail.com
Dim 30 Avr 21:50:00 EDT 2017


Hi all,

the next tea-talk will be given by Timnit Gebru from Stanford university.
The room will be AA5340, and the talk will start at 13:45.
Here is the detail of the talk.

*Title: *Using Deep Learning and Google Street View to Estimate the
Demographic Makeup of the US

*Abstract: *Targeted socio-economic policies require an accurate
understanding of a country’s demographic makeup. To that end, the United
States spends more than 1 billion dollars a year gathering census data such
as race, gender, education, occupation and unemployment rates. Compared to
the traditional method of collecting surveys across many years which is
costly and labor intensive, data-driven, machine learning driven approaches
are cheaper and faster—with the potential ability to detect trends in close
to real time. In this work, we leverage the ubiquity of Google Street View
images and develop a computer vision pipeline to predict income, per capita
carbon emission, crime rates and other city attributes from a single source
of publicly available visual data. We first detect cars in 50 million
images across 200 of the largest US cities and train a model to determine
demographic attributes using the detected cars. To facilitate our work, we
used a graph-based algorithm to collect the largest and most challenging
fine-grained dataset reported to date consisting of over 2600 classes of
cars comprised of images from Google Street View and other web sources. Our
prediction results correlate well with ground truth income (r=0.82), race,
education, voting, sources investigating crime rates, income segregation,
per capita carbon emission, and other market research. Finally, we learn
interesting relationships between cars and neighborhoods allowing us to
perform the first large-scale sociological analysis of cities using
computer vision techniques.

*Bio: *I am a PhD student in the Stanford Artificial Intelligence
Laboratory, studying computer vision under Fei-Fei Li. My main research
interest lies in data mining large-scale publicly available images to gain
sociological insight, and working on computer vision problems that arise as
a result. Some of these include fine-grained image recognition, scalable
annotation of images, and domain adaptation. Prior to joining Fei-Fei's lab
I worked at Apple designing circuits and signal processing algorithms for
various Apple products including the first iPad. I also spent an obligatory
year as an entrepreneur (as all Stanford undergrads seem to do). My
research is supported by the NSF foundation GRFP fellowship and currently
the Stanford DARE fellowship.

Best,
--Junyoung
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