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Bayesian Embeddings for Few-Shot Open World Recognition
[article]
2022
arXiv
pre-print
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information. This stands in stark contrast to modern machine learning systems that are typically designed with a known set of classes and a large number of examples for each class. In this work we extend embedding-based few-shot
arXiv:2107.13682v2
fatcat:fdylm542zvflfhf76bmijlyneu