prototypical-networks and prototypical-networks-tensorflow
The repositories are ecosystem siblings, where the first is the original PyTorch implementation of the research paper and the second is an independent TensorFlow reimplementation of the same paper, offering an alternative framework for the identical method.
About prototypical-networks
jakesnell/prototypical-networks
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
This code helps machine learning researchers and practitioners explore "few-shot learning" by implementing Prototypical Networks. It takes in small datasets (like Omniglot) where each class has only a few examples, and outputs a trained model capable of classifying new, unseen examples from those classes. This is for researchers working on novel machine learning approaches where data is scarce for many categories.
About prototypical-networks-tensorflow
abdulfatir/prototypical-networks-tensorflow
Tensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
This project helps machine learning researchers and practitioners explore "few-shot learning" methods. You can input image datasets, like Omniglot or Mini-ImageNet, and it will output trained models that can classify new images even when they've only seen a few examples of each class. This is ideal for those working on computer vision tasks with limited data.
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