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