A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"
License: MIT License
This repository contains PyTorch code for adversarial querying with ProtoNet, R2-D2, and MetaOptNet. Adversarial querying is an algorithm for producing robust meta-learners. More can be found in our NeurIPS 2020 paper. We adapt models and data loading from here.
Thank you for your code and paper! However,why remove maml-adml files,i found comparative experiment on this two models in your paper,but there are not maml-adml files in it.
could you provide training, testing and how to prepare dataset script for better understand?
Hi, thanks for your great work.
I wonder why in data/mini_imagenet.py, you don't use normalization.
Thanks for answering.
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