'Fast AutoAugment' by my team, KakaoBrain about deepaugment

[lichuanx]

We don't train child-netwo

Hey, nice and interesting work.
A question: if you don't searching a policy from a child networks and transfer to a big networks. The time using on training big networks such as inception-resnet_V2, will be a very slow. How to tackle this.
I have read your paper, but I can't figure out the answer, thank you.

[ildoonet]

1. We do search policies using small networks. We can use proxy network(small network) to search policies and then transfer the policies to the big ent. In our settings, experiment with Imagenet was conducted using small subset of dataset and resnet50 as a proxy network.

2. But we don't evaluate a policy by training child networks. We train a network just for one time and our algorithm evaluate many policies without tranining furthermore.

[lichuanx]

1. We do search policies using small networks. We can use proxy network(small network) to search policies and then transfer the policies to the big ent. In our settings, experiment with Imagenet was conducted using small subset of dataset and resnet50 as a proxy network.
2. But we don't evaluate a policy by training child networks. We train a network just for one time and our algorithm evaluate many policies without tranining furthermore.

Oh, thank you for your reply. That's very interesting idea.
Trying to figure it out in your paper that "minimize distance between density of DM and density of DA"
Augmentation is meant to let your train set match the distribution of val/test set. How's that searching a policy that change val set to match train set works. As for as I can concern, it is plays as some sort of regularization, that let's all your train set represent some "strong features" learned on your theta. Still little bit confused.