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

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hello, I run the code of baseline and baseline++ on CUB. However, the results are lower than the reported ones. python train.py --dataset CUB --model ResNet10 --method baseline++ --train_aug python...

Hi, Thanks for your contribution and the code is really well organized in a good writing style. From the code, I got the following understanding: Take miniImageNet and Protonet training...

In training stage of Baseline/Baseline++ method, it seems that you always set num_class as 200. But every dataset has specific number of base class(for example, miniImageNet has 64 base class)....

Hi, I re-do the Conv4-ProtoNet 5-way-5-shot experiment, but the accuracy is about 4% more than the paper. ```console python ./train.py --dataset CUB --model Conv4 --method protonet --train_aug python ./save_features.py --dataset...

hello, I ran your code on miniImageNet, when I use the dataset with image size 84*84, the results can not reach the reported accuracy, then I read you code which...

Hi there, Thanks for the great code and fantastic work during the ICLR 2019. Recently, I use the original code to test on Matching Network and found the result is...

Hi~ Thanks for your work! I found in your code, all the data is normalized before they are sent into the models. I want ask that is data normalization necessary...

![2019-08-08 10-54-40屏幕截图](https://user-images.githubusercontent.com/33992767/62671879-1d256380-b9cb-11e9-91eb-d4cab07a0f6a.png)

Hi : I am wondering if I want to train baseline++ with few shot training method, I just change the dataloader class ,right? Because now in train.py, the baseline and...

I try to run script 'download_miniImagenet.sh' and the program broke down with error messages below: --2019-05-27 09:53:56-- https://raw.githubusercontent.com/twitter/meta-learning-lstm/master/data/miniImagenet/test.csv%0D Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.76.133 Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.76.133|:443... connected. HTTP request sent,...