On the unreasonable effectiveness of CNNs

• URL: http://arxiv.org/abs/2007.14745v1
• Date: Wed, 29 Jul 2020 11:16:20 GMT
• Title: On the unreasonable effectiveness of CNNs
• Authors: Andreas Hauptmann and Jonas Adler
• Abstract summary: Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems. In an attempt to put upper bounds on the capability of baseline CNNs for solving image-to-image problems we applied a widely used standard off-the-shelf network architecture (U-Net) to the "inverse problem" of XOR decryption from noisy data and show acceptable results.
• Score: 7.673853485227739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks with ill-posed and complicated imaging models. In an attempt to put upper bounds on the capability of baseline CNNs for solving image-to-image problems we applied a widely used standard off-the-shelf network architecture (U-Net) to the "inverse problem" of XOR decryption from noisy data and show acceptable results.

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