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Discriminately Decreasing Discriminability with Learned Image Filters
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BibTeX
@MISC{Whitehill_discriminatelydecreasing,
author = {Jacob Whitehill and Javier Movellan},
title = {Discriminately Decreasing Discriminability with Learned Image Filters},
year = {}
}
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Abstract
In machine learning and computer vision, input signals are often filtered to increase data discriminability. For example, preprocessing face images with Gabor band-pass filters is known to improve performance in expression recognition tasks [1]. Sometimes, however, one may wish to purposely decrease discriminability of one classification task (a “distractor ” task), while simultaneously preserving information relevant to another task (the target task): For example, due to privacy concerns, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Suppressing discriminability in distractor tasks may also be needed to improve inter-dataset generalization: training datasets may sometimes contain spurious correlations between a target attribute (e.g., facial expression) and a distractor attribute (e.g., gender). We might improve generalization to new datasets by suppressing the signal related to the distractor task in the training dataset. This can be seen as a special form of supervised regularization. In this paper we present an approach to automatically learning preprocessing filters that suppress discriminability in distractor tasks while preserving it in target tasks. We present promising results in simulated image classification problems and in a realistic expression recognition problem. 1.
Keyphrases
distractor task learned image filter target task face image target attribute simulated image classification problem special form information relevant machine learning mechanical turk gabor band-pass filter training dataset crowdsourcing site computer vision new datasets facial expression classification task spurious correlation inter-dataset generalization privacy concern training datasets certain facial attribute input signal supervised regularization expression recognition task data discriminability distractor attribute realistic expression recognition problem
