Enhancing Generalization of First-Order Meta-LearningDownload PDF

Mar 22, 2019 (edited Jul 02, 2019)ICLR 2019 Workshop LLD Blind SubmissionReaders: Everyone
  • Keywords: meta-learning, generalization, few-shot learning
  • TL;DR: The study introduces two approaches to enhance generalization of first-order meta-learning and presents empirical evaluation on few-shot image classification.
  • Abstract: In this study we focus on first-order meta-learning algorithms that aim to learn a parameter initialization of a network which can quickly adapt to new concepts, given a few examples. We investigate two approaches to enhance generalization and speed of learning of such algorithms, particularly expanding on the Reptile (Nichol et al., 2018) algorithm. We introduce a novel regularization technique called meta-step gradient pruning and also investigate the effects of increasing the depth of network architectures in first-order meta-learning. We present an empirical evaluation of both approaches, where we match benchmark few-shot image classification results with 10 times fewer iterations using Mini-ImageNet dataset and with the use of deeper networks, we attain accuracies that surpass the current benchmarks of few-shot image classification using Omniglot dataset.
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