Audio Data Implementation of Matching Networks for One Shot Learning by Vinyals et al.
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Coding In Progress. Will update README soon!

Note on Omniglot Dataset

The dataset from Bredan Lake's github page gives a training split of 934 characters (30 alphabets) and evaluation split of 659 characters (20 alphabets). Both of Matching Networks and MANN use 1200 characters for training and 423 characters for evaluation. I was confused with this contradiction in the training/evaluation splits. Then I checked out the Siamese Networks paper by Koch and there he mentions the splits provided by Brendan Lake is 40 alphabets for training and 10 alphabets for evaluation. Another contradiction... Hence, to stick to 1200 characters for training and 423 for validation, I swapped some alphabets between the image_background and image_evaluation sets from Brendan Lake's original dataset, so that image_background (training split) has 1200 characters (40 alphabets) and image_evaluation (evaluation split) has 423 characters (10 alphabets). Phew! Providing the data here for reference.