"Open-Set Likelihood Maximization for Few-Shot Learning. (arXiv:2301.08390v1 [cs.CV])" — A generalization of the maximum likelihood principle, in which latent scores down-weighing the influence of potential outliers are introduced alongside the usual parametric model. This implementation can be applied on top of any pre-trained model seamlessly.

Paper: http://arxiv.org/abs/2301.08390

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Intuition behind OSLO. Standard…