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Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022), and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (Neural Networks 2022 - in submission)

License: MIT License

Python 79.53% Jupyter Notebook 19.25% Shell 1.21%
few-shot-learning few-shot-recognition few-shot-classifcation meta-learning meta-dataset mini-imagenet tiered-imagenet conditional-neural-process deep-learning metric-learning

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simple-cnaps's Issues

couldn't install meta-dataset==0.2.0

Hi Thanks for writing this repo

I came across an error when installing the requirement list.
it says ERROR: No matching distribution found for meta-dataset==0.2.0

pretrained file

Hello, when I decompressed the pretrained file package, the compressed file was damaged. If it is convenient for you, could you please send it again? Thank you very much!!

The results on Mini/Tiered ImageNet

I did a test following the instructions on mini/tiered Imagenet, I got some results on 1-shot setting,

miniImageNet: 80+/- 0.89
tieredImageNet: 86.2 +/- 0.79

For mini, it is a bit worse than the reported results (82.16), but tiered is much better (78.29).

Could you help to check these results, does it make sense?
Thanks.

How to Prediction

when I trained the simple-cnaps model, how to use the model to predict the new dataset?

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