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Fast Few-Shot Classification by Few-Iteration Meta-Learning
[article]
2022
arXiv
pre-print
Autonomous agents interacting with the real world need to learn new concepts efficiently and reliably. This requires learning in a low-data regime, which is a highly challenging problem. We address this task by introducing a fast optimization-based meta-learning method for few-shot classification. It consists of an embedding network, providing a general representation of the image, and a base learner module. The latter learns a linear classifier during the inference through an unrolled
arXiv:2010.00511v3
fatcat:tgf4j64oh5a6nojyw6p6l5sil4