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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Hi all,
we have a tea-talk today by Timnit Gebru! (AA5340,13:45). The detail of the talk is in my previous email.
Best, --Junyoung
On Sun, Apr 30, 2017 at 9:50 PM, Junyoung Chung elecegg@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
This talk will start in 30 minutes.
On Sun, Apr 30, 2017 at 9:50 PM Junyoung Chung elecegg@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
Tea-talk is staring in a minute!
--Junyoung
On Fri, May 5, 2017 at 1:10 PM, Junyoung Chung elecegg@gmail.com wrote:
This talk will start in 30 minutes.
On Sun, Apr 30, 2017 at 9:50 PM Junyoung Chung elecegg@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
lisa_seminaires@iro.umontreal.ca
-
Junyoung Chung