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

Junyoung Chung elecegg at gmail.com
Ven 5 Mai 13:39:00 EDT 2017


Tea-talk is staring in a minute!

--Junyoung

On Fri, May 5, 2017 at 1:10 PM, Junyoung Chung <elecegg at gmail.com> wrote:

> This talk will start in 30 minutes.
>
> On Sun, Apr 30, 2017 at 9:50 PM Junyoung Chung <elecegg at gmail.com> wrote:
>
>> 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
>>
> --
> --Junyoung
>
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