Few-shot learning is another challenging direction to explore. After learning on a set of base classes 
with abundant examples, new tasks are given with only few examples of novel (unseen) classes. For 
such cases, the learning strategy of multi-million parameters architectures in deep learning needs to be 
rethought in order to allow the networks to squeeze out the maximum amount of information from the 
few available samples.
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