Learning feed-forward one-shot learners

Internet publication

One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.

Authors: Bertinetto, L; Henriques, JF; Valmadre, J; Torr, PHS; Vedaldi, A

• Publication status: Published
• Peer review status: Not peer reviewed
• Host title: arXiv
• Publication date: 2016-06-16
• DOI: 10.48550/arxiv.1606.05233
• EISSN: 2331-8422
• Language: English