论文标题
稀疏代码对图像失真的敏感性
Sensitivity of sparse codes to image distortions
论文作者
论文摘要
稀疏编码已被提议作为视觉皮层理论，也是一种无监督的学习表征算法。我们用MNIST数据集在经验上表明，稀疏代码可能对图像扭曲非常敏感，这种行为可能会阻碍不变的对象识别。局部线性分析表明，灵敏度是由于存在高取消的活动字典元件的线性组合所致。显示最近的邻居分类器在稀疏代码上的性能要比原始图像差。对于具有足够数量的标记示例的线性分类器，稀疏代码显示出比原始图像更高的精度，但不高于随机前馈净计算的表示。对失真的敏感性似乎是稀疏代码的基本属性，当将稀疏代码应用于不变对象识别时，应该意识到此属性。
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.
