Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
- URL: http://arxiv.org/abs/2005.11450v1
- Date: Sat, 23 May 2020 02:10:57 GMT
- Title: Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
- Authors: Bijan Haney and Alexander Lavin
- Abstract summary: Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision.
We show how we can achieve few-shot fine-grain classification by maximally separating the classes while incorporating domain knowledge as informative priors.
- Score: 79.22051549519989
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prototypical networks have been shown to perform well at few-shot learning
tasks in computer vision. Yet these networks struggle when classes are very
similar to each other (fine-grain classification) and currently have no way of
taking into account prior knowledge (through the use of tabular data). Using a
spherical latent space to encode prototypes, we can achieve few-shot fine-grain
classification by maximally separating the classes while incorporating domain
knowledge as informative priors. We describe how to construct a hypersphere of
prototypes that embed a-priori domain information, and demonstrate the
effectiveness of the approach on challenging benchmark datasets for fine-grain
classification, with top results for one-shot classification and 5x speedups in
training time.
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