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Dear r/MachineLearning friends,
I just wanted to present my latest work to you. We, humans, have the ability to not only learn quickly from a few labelled examples, but we can also adjust our notions in light of new unlabelled data. I found this fascinating and wondered whether meta-learning an intrinsic loss function to do the same in the few-shot learning would, in fact, improve generalization performance. Turns out, it does. Any and all feedback is welcomed. You can be as harsh as you want. You can’t top reviewer #2 anyway.
Regards, Antreas
Paper: https://arxiv.org/abs/1905.10295
Code: Soon to follow (should be up before 29/05/2019)
submitted by /u/AntreasAntoniou
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