2019/10/08 by Zarka, John, Louis Thiry, Thiry, Louis +4 · 1 citation
Computer Science · Engineering · #Stochastic Gradient Optimization Techniques #Domain Adaptation and Few-Shot Learning #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1910.03561
We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The network first applies a scattering transform that linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse ℓ1 dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework that includes ALISTA. Classification results are analyzed on ImageNet.