论文标题
单图超分辨率的神经微分方程
Neural Differential Equations for Single Image Super-resolution
论文作者
论文摘要
尽管神经微分方程对诸如MNIST之类的玩具问题显示了希望，但尚未成功地应用于更具挑战性的任务。受依赖于部分微分方程的图像恢复方法的启发，我们选择基准在单个图像超分辨率上对几种形式的神经des和反向传播方法进行基准测试。先前提出的用于梯度估计的伴随方法没有理论稳定性的保证。我们发现了一种实用的情况，使其无法使用，并表明离散的灵敏度分析具有更好的稳定性。在我们的实验中，差异模型与最先进的超分辨率模型的性能相匹配。
Although Neural Differential Equations have shown promise on toy problems such as MNIST, they have yet to be successfully applied to more challenging tasks. Inspired by variational methods for image restoration relying on partial differential equations, we choose to benchmark several forms of Neural DEs and backpropagation methods on single image super-resolution. The adjoint method previously proposed for gradient estimation has no theoretical stability guarantees; we find a practical case where this makes it unusable, and show that discrete sensitivity analysis has better stability. In our experiments, differential models match the performance of a state-of-the art super-resolution model.
