Few Shot Learning with Code - Meta Learning - Prototypical Networks

This video addresses one of the biggest drawbacks of classical deep learning, the requirement for a large amount of data. Few-Shot Learning has become a popular method for many researchers to deal with this issue in recent years. The goal of few-shot learning (FSL) is to teach a machine to do something new with a significantly small amount of data.

I mentioned multiple popular algorithms that are used to solve FSL using the Meta-Learning framework. Then explained one of the popular algorithms called Prototypical Networks (NIPS’17, 3000+ citations) along with how to train and test FSL on Omniglot dataset in Google Colab using PyTorch.

Prototypical Networks classifies new classes (not part of training data/classes) based on their similarity to a small number of examples per class. This is one of the simplest algorithms to solve FSL. There are some other little more advanced algorithms like MAML, Meta-LSTM, and Reptile. I’ll cover them in future videos. Stay tuned. Thanks!
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Code Colab: https://colab.research.google.com/drive/1TPL2e3v8zcDK00ABqH3R0XXNJtJnLBCd?usp=sharing
Paper: https://arxiv.org/pdf/1703.05175.pdf
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Chapters
0:00 Intro: Issues with Classical Deep Learning
0:51 Alternative to Classical Learning: Few-Shot learning
2:18 Classical Learning vs Meta-Learning
5:47 Concept: Prototypical Networks
7:00 Code: Prototypical Networks - Train & Test
13:34 End
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#fewshotlearning #metalearning #prototypicalnetworks #computervision

Видео Few Shot Learning with Code - Meta Learning - Prototypical Networks канала Lights, Camera, Vision!