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The model has learnt the "features" for the type of inputs, eg. faces. For the problem to be called one-shot, it needs to also correctly classify/compare any new samples. For example, in face recognition application, any new person's images should also result a positive for their own image and negative for any other seen or unseen image. Since we are using ...


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ResNet is an architecture for object recognition and you may use it to do your classification task. Fast RCNN may improve your results but is a more difficult architecture to implement. If you want to go in this direction the best place to start is the arxiv paper of the Fast R-CNN (arxiv.org/abs/1504.08083). If I am not wrong, there is an implementation ...


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First of all, I would like to say that it is possible that these terms are used inconsistently, given that at least transfer learning, AFAIK, is a relatively new expression, so, the general trick is to take terminology, notation and definitions with a grain of salt. However, in this case, although it may sound confusing to you, all of the current ...


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The difference really comes down to the fact that in meta-learning, there is a population of tasks $\tau$ which have distribution $p(\tau)$. The goal is to perform well on a task drawn from $p(\tau)$. Generally 'perform well' means that with only a few training steps or data points, the model can give good classification accuracy, achieve high reward in an ...


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