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