A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.
By using a trick, meta-learning and meta-networks have become plug-and-play. We can now apply the meta learning algorithm directly to the existing pytorch model without rewriting it.
This code takes Meta-Weight-Net (Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting)
as an example to show how to use this trick. It rewrites an optimizer to assign non leaf node tensors to model parameters.
See meta.py and line 90-120 of main.py for details.
- python 3.8
- pytorch 1.9.0
- torchvision 0.10.0
noisy_long_tail_CIFAR.py can generate noisy and long-tailed CIFAR datasets by calling torchvision.datasets. Because
some class attributes' names have been changed, errors may occur in some earlier versions of torchvision. It can be solved by
changing the corresponding attribute name.
ResNet32 on CIFAR10-LT with imbalanced factor of 50:
python main.py --imbalanced_factor 50
ResNet32 on CIFAR10 with 40% uniform noise:
python main.py --meta_lr 1e-3 --meta_weight_decay 1e-4 --corruption_type uniform --corruption_ratio 0.4
| Data Setting | Test Accuracy |
|---|---|
| imbalanced factor 50 | 80.43% |
| imbalanced factor 100 | 75.92% |
| imbalanced factor 200 | 68.89% |
| 40% uniform noise | 87.83% |
Thanks to the original code of Meta-Weight-Net (https://github.com/xjtushujun/meta-weight-net).
Contact: Shi Yunyi ([email protected])
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