Sensitivity of sparse codes to image distortions

• URL: http://arxiv.org/abs/2204.07466v1
• Date: Fri, 15 Apr 2022 13:58:00 GMT
• Title: Sensitivity of sparse codes to image distortions
• Authors: Kyle Luther, H. Sebastian Seung
• Abstract summary: We show that sparse codes can be very sensitive to image distortions. The sensitivity is due to the existence of linear combinations of active dictionary elements with high cancellation.
• Score: 4.209801809583906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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