Learnable Sparse Coding and Its Application to Inverse Problems

除了需要地面实况解决方案来完成迭代过程之外，我们已经实现了第二年的目标，即开发无监督学习算法，该算法扩展了可学习稀疏编码算法的能力，这些算法主要是用L1范数迭代收缩阈值算法（ISTA）和提出的非凸约束算法构建的。反问题实验的实时训练和实现。我们的约束，包括凸和非凸的第一年期间，同时也探讨了最近的约束来自最新的研究工作的整合。

Beyond requiring the ground-truth solution to complete the iteration procedure, we have achieved the second year’s goal to develop the unsupervised learning algorithms which extend the ability of the Learnable Sparse Coding algorithms which are mainly built with L1-norm iterative shrinkage-thresholding algorithms (ISTA) and the proposed non-convex constraint algorithms. The real-time training and implementation for the inverse problem experiments are performed. Our constraints incorporate both the convex and non-convex ones employed during the first year, while also exploring the integration of recent constraints derived from the latest research efforts.