prototypical-network-pytorch and prototypical-networks-tensorflow
These two tools are competitors, as they offer independent implementations of the same foundational research paper, "Prototypical Networks for Few-shot Learning," but using different deep learning frameworks (PyTorch and TensorFlow).
About prototypical-network-pytorch
yinboc/prototypical-network-pytorch
A re-implementation of "Prototypical Networks for Few-shot Learning"
This project helps machine learning researchers and practitioners explore 'few-shot learning' scenarios. It takes image datasets with limited examples per category and trains a model to classify new, unseen images with high accuracy, even when only one or a few examples of that category were available during training. This is useful for those developing or evaluating classification systems under data scarcity.
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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