In this example, we re-implement Meta-Learning with Implicit Gradients
We rely on learn2learn for the data loading part. Please install
learn2learn before running the main training script.
pip install learn2learn- Shots: [1, 5]
- Ways: [5, 20]
bash omniglot.sh- Shots: [1, 5]
- Ways: 5
bash mini_imagenet.shAs our goal is to demonstrate how to implement (i)MAML with Betty, we didn't do extensive hyperparameter tuning in our experiments. Also, we used DARTS algorithm for hypergradient calculation, which achieves better computation/memory efficiency than conjugate gradient (CG) algorithm in the original paper. Further performance improvement can be achieved by trying out different hypergradient algorithms and hyperparameters.
| 5-way 1-shot | 5-way 5-shot | 20-way 1-shot | 20-way 5-shot | |
|---|---|---|---|---|
| MAML | 98.7% | 99.9% | 95.8% | 98.9% |
| FOMAML | 98.3% | 99.2% | 89.4% | 97.9% |
| Reptile | 97.68% | 99.48% | 89.43% | 97.12% |
| iMAML (orig) | 99.16% | 99.67% | 94.46% | 98.69% |
| iMAML (Betty) | 98.68% | 99.58% | 92.90% | 98.04% |
To be added.