About hyper-parameter about supervised-reptile HOT 8 CLOSED

Hi @jaegerstar, thanks for the discussion.

In a paper named "Deep Meta-Learning: Learning to Learn in the Concept Space", they used maml-style update and applied ResNet as the CNN structure. And they gained improvements compared with original MAML paper.

The difference is that they only meta-update the last few layers for ResNet.

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Ah, thanks for reminding me to fix this. At one point, I updated the HPs (and results) in the paper to be simpler, because I found that they didn't make much of a difference. I never changed them in this repo.

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Thanks for the reply.

Very insightful paper for saving GPU memory and computation cost.
Hope to see more results on large network structures such as ResNet!

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@csyanbin I doubt it would work better in larger network because it brings more weights as it could increase the risk of overfitting. That why the former work MAML reduced the number of convolutional filters .

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So -- just to make sure I'm translating parameters from the paper to code correct, the parameters for the omniglot experiments would look like this?

# 1-shot
python -u run_omniglot.py \
    --train-shots 10 \
    --inner-batch 10 \
    --inner-iters 5 \
    --shots 1 \
    --eval-batch 5 \
    --eval-iters 50 \
    --meta-batch 5 \
    --meta-iters 100000 \
    --learning-rate 0.001 \
    --meta-step 1 \
    --meta-step-final 0    

# 5-shot
python -u run_omniglot.py \
    --train-shots 10 \
    --inner-batch 20 \
    --inner-iters 10 \
    --shots 5 \
    --eval-batch 10 \
    --eval-iters 50 \
    --meta-batch 5 \
    --meta-iters 200000 \
    --learning-rate 0.0005 \
    --meta-step 1 \
    --meta-step-final 0   

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@csyanbin thanks, I will take a look.

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@bkj I see one problem--your 5-shot arguments should have --shots 5. Also, you may want to pass --transduction depending on which experiment you want to reproduce.

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Ah yeah -- good catch. Updated above.

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