Incremental Meta-Learning via Indirect Discriminant Alignment

11 Feb 2020Qing LiuOrchid MajumderAlessandro AchilleAvinash RavichandranRahul BhotikaStefano Soatto

Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase. To the best of our knowledge, all such meta-learning methods use a single base dataset for meta-training to sample tasks from and do not adapt the algorithm after meta-training... (read more)

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