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] What is the difference between few-, one- and zero-shot learning?

At first, I’ve thought that:

– few-shot learning is when there is only few training examples for each label available;

– one-shot learning is when there might be only one training example for a label;

– zero-shot learning is when some labels won’t be available in training sample.

But, for example, in Siamese Neural Networks for One-shot Image Recognition training process requires more than one training example for label in set, which would be few-shot learning, and in the test time you can choose the class which was not represented, which would be zero-shot learning.

It confuses me and I will greatly appreciate if someone helps me.

submitted by /u/FeatherNox839
[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.