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.

Usage

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

metaSGD and MAML

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.