Deep Network Classification by Scattering and Homotopy Dictionary LearningDownload PDF

Sep 25, 2019 (edited Mar 11, 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: A scattering transform followed by supervised dictionary learning reaches a higher accuracy than AlexNet on ImageNet.
  • Abstract: 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 $\ell^1$ 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.
  • Keywords: dictionary learning, scattering transform, sparse coding, imagenet
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