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arXiv · 2204.07466

Sensitivity of sparse codes to image distortions

Kyle Luther·H. Sebastian Seung
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Abstract

Sparse coding has been proposed as a theory of visual cortex and as an unsupervised algorithm for learning representations. We show empirically with the MNIST dataset that sparse codes can be very sensitive to image distortions, a behavior that may hinder invariant object recognition. A locally linear analysis suggests that the sensitivity is due to the existence of linear combinations of active dictionary elements with high cancellation. A nearest neighbor classifier is shown to perform worse on sparse codes than original images. For a linear classifier with a sufficiently large number of labeled examples, sparse codes are shown to yield higher accuracy than original images, but no higher than a representation computed by a random feedforward net. Sensitivity to distortions seems to be a basic property of sparse codes, and one should be aware of this property when applying sparse codes to invariant object recognition.

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Kyle Luther, H. Sebastian Seung. 2022-04-15. Sensitivity of sparse codes to image distortions. https://arxiv.org/abs/2204.07466

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