Deep Network classification by Scattering and Homotopy dictionary learning

Deep convolutional networks have spectacular applications to classification and regression (LeCun et al., 2015), but they are a black box which are hard to analyze mathematically because of their architecture Despite its simplicity, it applies to complex image classification and reaches a higher accuracy than AlexNet (Krizhevsky et al., 2012) over ImageNet ILSVRC2012. It is implemented with a deep convolutional network architecture. Dictionary learning for classification was introduced in Mairal et al. (2009) and implemented with deep A major issue is to compute the sparse code with a small network. We introduce a new architecture based on homotopy continuation, which leads to exponential convergence. The ALIST A (Liu et al., 2019) sparse code is incorporated in We explain the implementation and mathematical properties of each element of the sparse scattering network.