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An implementation of Model Agnostic Meta Learning (MAML) for few shot supervised image classification.

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University of Pisa, UNIPI
Computer Science Department
Authors: Irene Pisani
July, 2023

Continual Learning

Final project for CL course (Prof. Antonio Carta)

An implementation of Model Agnostic Meta Learning for few shot supervised image classification

Project objective

  1. Provide an implementation of MAML for few shot supervised learning using Pytorch.
  2. Reproduce the experiments on the Omniglot dataset. Follow the original experimental protocol of N-way classification with 1 or 5 shots and N equal to 5 or 20.
  3. Performance comparison between the obtained performance and original ones.
  4. Analysis of the impact of the number of inner SGD steps during training and evaluation.

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An implementation of Model Agnostic Meta Learning (MAML) for few shot supervised image classification.

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