Matching Networks for One Shot Learning
It addresses the problem of rapid learning from limited data for machine learning practitioners, offering a novel approach that is not incremental but introduces a new paradigm.
The paper tackles the challenge of learning new concepts from few examples by proposing a framework that maps a small labeled support set and an unlabeled example to its label, eliminating the need for fine-tuning. It improves one-shot accuracy on ImageNet from 87.6% to 93.2% and on Omniglot from 88.0% to 93.8%, and demonstrates applicability to language modeling on the Penn Treebank.
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.