论文标题
关于CNN的不合理有效性
On the unreasonable effectiveness of CNNs
论文作者
论文摘要
使用卷积神经网络（CNN）的深度学习方法已成功地应用于几乎所有成像问题，尤其是在具有不良且复杂的成像模型的图像重建任务中。为了将基线CNN的能力置于求解图像到图像问题的能力上，我们将广泛使用的标准现成网络体系结构（U-NET）应用于噪声数据的XOR解密的“反向问题”，并显示可接受的结果。
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.
