Blog

Learn About Our Meetup

5000+ Members

MEETUPS

LEARN, CONNECT, SHARE

Join our meetup, learn, connect, share, and get to know your Toronto AI community. 

JOB POSTINGS

INDEED POSTINGS

Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.

CONTACT

CONNECT WITH US

Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[D] Learning with “noisy data” (but perfect labels)

There are many works that deal with noisy labels, but has the problem of unreliable data (but reliable labels) been studied? In other words, problems where the data to be classified is imperfect and not always sufficient to determine the class label.

An example would be a model that predicts the city in which a photo was taken. Ground truth labels would be perfect thanks to GPS metadata. If the photo contains the Eiffel Tower, we can predict that the city is Paris. But many pictures contain no useful information; for example a photo of a dog or a McDonald’s is nearly useless for determining the city.

How best to train a classifier when such “noisy examples” (for lack of a better term) are very common?

submitted by /u/viviandefeater
[link] [comments]

Next Meetup

 

Days
:
Hours
:
Minutes
:
Seconds

 

Plug yourself into AI and don't miss a beat

 


Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.