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Separating style and content with bilinear models (2000)

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by Joshua B. Tenenbaum , William T. Freeman
Venue:NEURAL COMPUTATION
Citations:223 - 3 self
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BibTeX

@ARTICLE{Tenenbaum00separatingstyle,
    author = {Joshua B. Tenenbaum and William T. Freeman},
    title = {Separating style and content with bilinear models},
    journal = {NEURAL COMPUTATION},
    year = {2000},
    pages = {1247--1283}
}

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Abstract

PERCEPTUAL systems routinely separate content from style, classifying familiar words spoken in an unfamiliar accent, identifying a font or handwriting style across letters, or recognizing a familiar face or object seen under unfamiliar viewing conditions. Yet a general and tractable computational model of this ability to untangle the underlying factors of perceptual observations remains elusive. Existing factor models are either insufficiently rich to capture the complex interactions of perceptually meaningful factors such as phoneme and speaker accent or letter and font, or do not allow efficient learning algorithms. Here we show how perceptual systems may learn to solve these crucial tasks using surprisingly simple bilinear models. We report promising results in three realistic perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.

Keyphrases

bilinear model    perceptual system    complex interaction    perceptual observation    benchmark multi-speaker database    simple bilinear model    unseen letter    tractable computational model    familiar face    separate content    familiar word    vowel classification    realistic perceptual domain    speaker accent    factor model    meaningful factor    underlying factor    crucial task    unfamiliar accent   

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