Learning From One Example Through Shared Densities On Transforms
An alternative to the Bayesian One-Shot Learning algorithm, the algorithm presented by Erik Miller, Nicholas Matsakis, and Paul Viola at ICCV 2000 uses knowledge transfer by model parameters to learn a new object category which is similar in appearance to previously learnt categories. In their paper, an image is represented as either a texture and shape, or as a latent image which has been transformed, denoted by .
Read more about this topic: One-shot Learning
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