S Emi - S Upervised F Ew - S Hot L Earning with Maml
نویسندگان
چکیده
We present preliminary results on extending Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017a) to fast adaptation to new classification tasks in the presence of unlabeled data. Using synthetic data, we show that MAML can adapt to new tasks without any labeled examples (unsupervised adaptation) when the new task has the same output space (classes) as the training tasks do. We further extend MAML to the semi-supervised few-shot learning scenario, when the output space of the new tasks can be different from the training tasks.
منابع مشابه
Iclr 2018 F Ew - S Hot L Earning with G Raph N Eural N Et - Works
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recentl...
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