论文标题
通过学习的时间序列表示可解释的超分辨率
Interpretable Super-Resolution via a Learned Time-Series Representation
论文作者
论文摘要
我们开发一种可解释且可学习的Wigner-Ville分布，该分布产生超级分辨的二次信号表示，以进行时间序列分析。我们的方法有两个主要标志。首先，它在已知的时频表示（TFR）之间进行了插值，因为它可以达到超级分辨率，而超出了海森堡不确定性原理的时间和频率解决方案，因此超出了常见的TFR，其次，其第二，由于明确的低维和物理参数，它是可以解释的。我们证明，我们的方法能够学习高度适应的TFR，并准备好并且能够应对各种大规模分类任务，与基准和学识渊博的TFR相比，我们达到了最先进的性能。
We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis. Our approach has two main hallmarks. First, it interpolates between known time-frequency representations (TFRs) in that it can reach super-resolution with increased time and frequency resolution beyond what the Heisenberg uncertainty principle prescribes and thus beyond commonly employed TFRs, Second, it is interpretable thanks to an explicit low-dimensional and physical parameterization of the Wigner-Ville distribution. We demonstrate that our approach is able to learn highly adapted TFRs and is ready and able to tackle various large-scale classification tasks, where we reach state-of-the-art performance compared to baseline and learned TFRs.
