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A collection of Gradient-Based Meta-Learning Algorithms with pytorch

License: MIT License

Python 100.00%

gradient-based-meta-learning few-shot-learning maml cavia reptile pytorch meta-learning implicit-maml neumann-approximation meta-learning-algorithms

gbml's Introduction

GBML

A collection of Gradient-Based Meta-Learning Algorithms with pytorch

MAML

python3 main.py --alg=MAML

Reptile

python3 main.py --alg=Reptile

CAVIA

python3 main.py --alg=CAVIA

Results on miniImagenet

Without pre-trained encoder (Use 64 channels by default. The exceptions are in parentheses)

	5way 1shot	5way 1shot (ours)	5way 5shot	5way 5shot (ours)
MAML	48.70 ± 1.84%	49.00 %	63.11 ± 0.92%	65.18 %
Reptile	47.07 ± 0.26%	43.40 %	62.74 ± 0.37%	-
CAVIA	49.84 ± 0.68% (128)	50.07 % (64)	64.63 ± 0.53% (128)	64.21 % (64)
iMAML	49.30 ± 1.88%	-	-	-
Meta-Curvature	55.73 ± 0.94% (128)	-	70.30 ± 0.72% (128)	-

With pre-trained encoder (To be implemented.)

	5way 1shot	5way 1shot (ours)	5way 5shot	5way 5shot (ours)
Meta-SGD	56.58 ± 0.21%	-	68.84 ± 0.19%	-
LEO	61.76 ± 0.08%	-	77.59 ± 0.12%	-
Meta-Curvature	61.85 ± 0.10%	-	77.02 ± 0.11%	-

Dependencies

Python >= 3.6
Pytorch >= 1.2
Higher
Torchmeta

To do

Add ~~ResNet~~ and Pre-trained encoder
Add iMAML, Meta-Curvature

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