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

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GBML

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

python3 main.py --alg=MAML
python3 main.py --alg=Reptile
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

To do

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