Awesome
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