Meta-SGD(Meta-SGD: Learning to Learn Quickly for Few Shot Learning(Zhenguo Li et al.)) experiment on Omniglot classification compared with MAML(Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (Finn et al., ICML 2017))
code from MAML
data from Omniglot
tips: some difference with the paper Meta-SGD: Learning to Learn Quickly for Few Shot Learning(Zhenguo Li et al.), the meta-update datas do not come from the seperate dataset.
python main.py --datasource=omniglot --metatrain_iterations=40000 --meta_batch_size=32 --update_batch_size=1 --update_lr=0.4 --num_updates=1 --logdir=logs/omniglot5way/
python main.py --datasource=omniglot --metatrain_iterations=40000 --meta_batch_size=32 --update_batch_size=1 --update_lr=0.4 --num_updates=1 --logdir=logs/omniglot5way/ --train=False --test_set=True
all the x label in the figure is iteration step.
considering the time cost other than the iteration step:
- we can see that the convergence speed and performance of metaSGD is better than MAML
- the result in both iteration and time scale is the same
- other than MAML, performance of meta-SGD won't get worst in long-term training.
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