Thursday, August 10, 2017

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Learning artistic intuition from human and machine expertise
Event Date:

Abstract:

How might we teach machine learning (ML) systems about what wine tastes like, or how to appreciate the similarities in different kinds of artwork? On its face, this question seems absurd because these notions of similarity are impossible to characterize in meaningful ways. Our work explores what happens when we can embrace this ambiguity. We use new kinds of semi-supervision to learn abstract, intuitive notions of perceptual similarity when labels or dense similarity measures are not available.