Separating Style and Content with Bilinear Models

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 (Hofstadter, 1985). Existing factor models (Mardia, Kent, & Bibby, 1979; Hinton & Zemel, 1994; Ghahramani, 1995; Bell & Sejnowski, 1995; Hinton, Dayan, Frey, & Neal, 1995; Dayan, Hinton, Neal, & Zemel, 1995; Hinton & Ghahramani, 1997) 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. We present a general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nonetheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization. We report promising results on three different tasks in three different perceptual domains: spoken vowel classification with a benchmark multi-speaker database, extrapolation of fonts to unseen letters, and translation of faces to novel illuminants.

DOI: 10.1162/089976600300015349

Extracted Key Phrases

12 Figures and Tables

0204060'02'04'06'08'10'12'14'16
Citations per Year

624 Citations

Semantic Scholar estimates that this publication has 624 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Tenenbaum2000SeparatingSA, title={Separating Style and Content with Bilinear Models}, author={Joshua B. Tenenbaum and William T. Freeman}, journal={Neural computation}, year={2000}, volume={12 6}, pages={1247-83} }