One-Shot Learning Task
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An One-Shot Learning Task is an automated learning task that aims to learn patterns from one, or only a few, training images.
- Counter-Example(s):
- See: Generative Model, Variational Bayesian Methods.
References
2018
- (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/one-shot_learning Retrieved:2018-11-19.
- One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images.
The primary focus of this article will be on the solution to this problem presented by Fei-Fei Li, R. Fergus and P. Perona in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol28(4), 2006, which uses a generative object category model and variational Bayesian framework for representation and learning of visual object categories from a handful of training examples. Another paper, presented at the International Conference on Computer Vision and Pattern Recognition (CVPR) 2000 by Erik Miller, Nicholas Matsakis, and Paul Viola will also be discussed.
- One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images.