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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—William Butler Yeats (1865–1939)
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“Now, since our condition accommodates things to itself, and transforms them according to itself, we no longer know things in their reality; for nothing comes to us that is not altered and falsified by our Senses. When the compass, the square, and the rule are untrue, all the calculations drawn from them, all the buildings erected by their measure, are of necessity also defective and out of plumb. The uncertainty of our senses renders uncertain everything that they produce.”
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