非参数变换网络
Non-Parametric Transformation Networks
摘要：
ConvNets通过其架构，只强制翻译的不变性。在本文中，我们介绍了一类新的深度卷积架构，称为非参数变换网络（NPTNs），它可以直接从数据中学习\textit{general}不变性和对称性。NPTNs是ConvNets的自然推广，可以直接使用梯度下降进行优化。与几乎所有以前在深度架构中的工作不同，它们没有假设数据中存在的不变性的结构，并且在这方面是灵活和强大的。我们还在一个名为转换网络（TN）的统一框架下对ConvNets和NPTNs进行建模，这可以更好地理解两者之间的联系。我们证明了NPTNs在数据上的有效性，例如具有极端变换的MNIST和CIFAR 10，它们的性能优于基线，并进一步优于ETH-80上的几种最新算法。他们这样做，同时具有相同数量的参数。我们还表明，它们比ConvNets更有效地从数据中建模对称性和不变性，而无需明确了解添加的任意滋扰变换。最后，我们在胶囊网络中用NPTN取代了ConvNets，并表明这使胶囊网络的性能更好。
英文摘要：
ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn \textit{general} invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks (TN), which yields a better understanding of the connection between the two. We demonstrate the efficacy of NPTNs on data such as MNIST with extreme transformations and CIFAR10 where they outperform baselines, and further outperform several recent algorithms on ETH-80. They do so while having the same number of parameters. We also show that they are more effective than ConvNets in modelling symmetries and invariances from data, without the explicit knowledge of the added arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs within Capsule Networks and show that this enables Capsule Nets to perform even better.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
