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===Model-Based===
Model-based meta-learning models updates its parameters rapidly with a few training steps, which can be achieved by its internal architecture or controlled by another meta-learner model.<ref name="paper1"/>
====Memory-Augmented Neural Networks====
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===Metric-Based===
The core idea in metric-based meta-learning is similar to [[K-nearest neighbor algorithm|nearest neighbors]] algorithms, which weight is generated by a kernel function. It aims to learn a metric or distance function over objects. The notion of a good metric is problem-dependent. It should represent the relationship between inputs in the task space and facilitate problem solving.<ref name="paper1" />
====Convolutional Siamese [[Neural Network]]====
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===Optimization-Based===
What optimization-based meta-learning algorithms intend for is to adjust the [[optimization algorithm]] so that the model can be good at learning with a few examples.<ref name="paper1"/>
====LSTM Meta-Learner====
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