One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples. The term few-shot learning is also used for these problems, especially when more than one example is needed.

The ability to learn object categories from few examples, and at a rapid pace, has been demonstrated in humans. It is estimated that a child learns almost all of the 10 ~ 30 thousand object categories in the world by age six. This is due not only to the human mind's computational power, but also to its ability to synthesize and learn new object categories from existing information about different, previously learned categories. Given two examples from two object categories: one, an unknown object composed of familiar shapes, the second, an unknown, amorphous shape; it is much easier for humans to recognize the former than the latter, suggesting that humans make use of previously learned categories when learning new ones. The key motivation for solving one-shot learning is that systems, like humans, can use knowledge about object categories to classify new objects.

As with most classification schemes, one-shot learning involves three main challenges:

One-shot learning differs from single object recognition and standard category recognition algorithms in its emphasis on knowledge transfer, which makes use of previously learned categories.

The Bayesian one-shot learning algorithm represents the foreground and background of images as parametrized by a mixture of constellation models. During the learning phase, the parameters of these models are learned using a conjugate density parameter posterior and Variational Bayesian Expectation–Maximization (VBEM). In this stage the previously learned object categories inform the choice of model parameters via transfer by contextual information. For object recognition on new images, the posterior obtained during the learning phase is used in a Bayesian decision framework to estimate the ratio of p(object | test, train) to p(background clutter | test, train) where p is the probability of the outcome.

Given the task of finding a particular object in a query image, the overall objective of the Bayesian one-shot learning algorithm is to compare the probability that object is present vs the probability that only background clutter is present. If the former probability is higher, the algorithm reports the object's presence, otherwise the algorithm reports its absence. To compute these probabilities, the object class must be modeled from a set of (1 ~ 5) training images containing examples.

To formalize these ideas, let ${\displaystyle I}$ be the query image, which contains either an example of the foreground category ${\displaystyle O_{fg}}$ or only background clutter of a generic background category ${\displaystyle O_{bg}}$. Also let ${\displaystyle I_{t}}$ be the set of training images used as the foreground category. The decision of whether ${\displaystyle I}$ contains an object from the foreground category, or only clutter from the background category is:

where the class posteriors ${\displaystyle p(O_{fg}|I,I_{t})}$ and ${\displaystyle p(O_{bg}|I,I_{t})}$ have been expanded by Bayes' Theorem, yielding a ratio of likelihoods and a ratio of object category priors. We decide that the image ${\displaystyle I}$ contains an object from the foreground class if ${\displaystyle R}$ exceeds a certain threshold ${\displaystyle T}$. We next introduce parametric models for the foreground and background categories with parameters ${\displaystyle \theta }$ and ${\displaystyle \theta _{bg}}$ respectively. This foreground parametric model is learned during the learning stage from ${\displaystyle I_{t}}$, as well as prior information of learned categories. The background model we assume to be uniform across images. Omitting the constant ratio of category priors, ${\displaystyle {\frac {p(O_{fg})}{p(O_{bg})}}}$, and parametrizing over ${\displaystyle \theta }$ and ${\displaystyle \theta _{bg}}$ yields