Few Shot Learning with Code Meta Learning Prototypical Networks

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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! • -------------------- • ✅👍📸 • Subscribe to the Channel 👉 / @lightscameravision • -------------------- • Code Colab: https://colab.research.google.com/dri... • Paper: https://arxiv.org/pdf/1703.05175.pdf • -------------------- • 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 • -------------------- • #fewshotlearning #metalearning #prototypicalnetworks #computervision

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