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Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
[article]
2020
arXiv
pre-print
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks. Existing meta-learning approaches have shown promising empirical performance on various multiclass classification problems, but few provide theoretical analysis on the classifiers' generalization ability on future tasks. In this paper, under the assumption that all classification tasks are sampled from the same meta-distribution, we leverage margin theory and statistical
arXiv:2012.01602v1
fatcat:vlkkgeqnm5codeuzwlcdwk7gsq