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• W2947883632 abstract "Few-shot models have become a popular topic of research in the past years. They offer the possibility to determine belongings for unseen examples using just a handful of examples for each class. Such models are trained on a wide range of classes and their respective examples, learning a decision metric in the process. Types of few-shot models include matching networks and prototypical networks. We show a new way of training prototypical few-shot models for just a single class. These models have the ability to predict the likelihood of an unseen query belonging to a group of examples without any given counterexamples. The difficulty here lies in the fact that no relative distance to other classes can be calculated via softmax. We solve this problem by introducing a null class centered around zero, and enforcing centering with batch normalization. Trained on the commonly used Omniglot data set, we obtain a classification accuracy of .98 on the matched test set, and of .8 on unmatched MNIST data. On the more complex MiniImageNet data set, test accuracy is .8. In addition, we propose a novel Gaussian layer for distance calculation in a prototypical network, which takes the support examples' distribution rather than just their centroid into account. This extension shows promising results when a higher number of support examples is available." @default.
• W2947883632 created "2019-06-07" @default.
• W2947883632 creator A5050670407 @default.
• W2947883632 date "2019-09-25" @default.
• W2947883632 modified "2023-09-27" @default.
• W2947883632 title "One-way prototypical networks" @default.
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