Learning feed-forward one-shot learners
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 propo...
| Main Authors: | , , , , |
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| Format: | Conference item |
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Massachusetts Institute of Technology Press
2016
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| _version_ | 1797096675715579904 |
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| author | Bertinetto, L Henriques, J Valmadre, J Torr, P Vedaldi, A |
| author_facet | Bertinetto, L Henriques, J Valmadre, J Torr, P Vedaldi, A |
| author_sort | Bertinetto, L |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:44:55Z |
| format | Conference item |
| id | oxford-uuid:d2e7d108-0f6d-46d6-8d16-1457027d3123 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:44:55Z |
| publishDate | 2016 |
| publisher | Massachusetts Institute of Technology Press |
| record_format | dspace |
| spelling | oxford-uuid:d2e7d108-0f6d-46d6-8d16-1457027d31232022-03-27T08:07:32ZLearning feed-forward one-shot learnersConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d2e7d108-0f6d-46d6-8d16-1457027d3123Symplectic Elements at OxfordMassachusetts Institute of Technology Press2016Bertinetto, LHenriques, JValmadre, JTorr, PVedaldi, AOne-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. |
| spellingShingle | Bertinetto, L Henriques, J Valmadre, J Torr, P Vedaldi, A Learning feed-forward one-shot learners |
| title | Learning feed-forward one-shot learners |
| title_full | Learning feed-forward one-shot learners |
| title_fullStr | Learning feed-forward one-shot learners |
| title_full_unstemmed | Learning feed-forward one-shot learners |
| title_short | Learning feed-forward one-shot learners |
| title_sort | learning feed forward one shot learners |
| work_keys_str_mv | AT bertinettol learningfeedforwardoneshotlearners AT henriquesj learningfeedforwardoneshotlearners AT valmadrej learningfeedforwardoneshotlearners AT torrp learningfeedforwardoneshotlearners AT vedaldia learningfeedforwardoneshotlearners |