Image recognition with deep learning

Project ID
Project Categories
Computer Science
Project Alumni
Kai Zhen (zhenk)
XUAN DONG (xuandong)
Achyut Sarma Boggaram (achbogga)
mridul birla (mbirla)
Sven Bambach (sbambach)
Ali Varamesh (alivara)
Ramya Rao (ramyarao)
Jacob Beauchamp (jacobeau)
Siddarth Jayamoorthy (sidjayam)
Shashi Shankar (shashank)
Gurleen Dhody (gdhody)
Yiwei Wei (yiwwei)
Luke Lovett (lalovett)
Tousif Ahmed (eshan077)
Katie Spoon (kspoon)
Javier Fuentes-Rohwer (fuentjav)
Charles Tostaine (ctostain)
Paritosh Morparia (pmorpari)
Every day, millions of people across the world take photos and upload them to social media websites. Their goal is to share photos with friends and others, but collectively they are creating vast repositories of visual information about the world. Each photo is an observation of how the world looked at a particular point in time and space. Aggregated together, these photos could provide new sources of observational data for use in disciplines like biology, earth science, social science or history. This project is investigating the algorithms and technologies needed for mining these large collections of photographs and noisy metadata to draw inferences about the physical world. The project has four research thrusts: (1) investigating techniques for identifying and correcting noise in metadata like geo-tags and timestamps, (2) developing algorithms for extracting semantic information from images and metadata, (3) creating methods for robust aggregation of noisy evidence from multiple photos, (4) validating these techniques on interdisciplinary applications in biology, sociology, and earth science.
Use of FutureSystems
We will primarily use GPU nodes for deep learning applications.
Scale of Use
Generally just a handful of GPUs; more for relatively rare large-scale experiments.