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source code to ICLR'19, 'A Closer Look at Few-shot Classification'

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hello, very nice work~ I am running the demo with CUB data, however, in test stage, an error happend:(And I found that the code just updated a day ago~) Traceback...

Getting around 80.78 (85.17 in paper) % accuracy on 5 shot , ResNet10 baseline++ with augmentation on. **Command used to train** : ```python train.py --dataset CUB --model ResNet10 --method baseline++...

Hi there, If I have the right understanding that for the baseline and baseline++ model trained with base classes, in the paper reported results the model used is the epoch399...

Does the learning rate always stay the same (default 0.001 in the Adam optimizer)? If not, could you tell me how you set in your experiments? Thanks!

I have train your code with loss_type = 'dist' & train_aug(baseline++),but the 5 shot accuracy on mini-imagenet is only 56%,much lower than your report(66%),could you give a resonable explaination?

Hi, I wanted to know if you had run baseline/baseline++ over the tiered ImageNet dataset. If so, can you share the performance results over it also?

Hi, It is a nice work. Now I am facing some problems when running your code. So I want to make sure that I use the same pytorch as you....

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 21 and 7 in dimension 1 at /opt/conda/conda-bld/pytorch_1549635019666/work/aten/src/TH/generic/THTensorMoreMath.cpp:1307