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The training step uses Algorithm 1 to solve the inverse prob- lem, and then updates the filters with stochastic gradient de- scent following Section 4

EXPERIMENTAL RESULTS To illustrate our results, we trained a set of hierarchical models withL = 3 on MNIST database, comprising 60,000 training, 10,000 test grayscale digit

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Convolutional Dictionary Learning in Hierarchical Networks

cs.LG · 2019-07-23 · unverdicted · novelty 5.0

A hierarchical convolutional dictionary learning model for piecewise smooth signals using recursive scale-detail filtering and sparse coding, learned by alternating minimization and demonstrated on MNIST.

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  • Convolutional Dictionary Learning in Hierarchical Networks cs.LG · 2019-07-23 · unverdicted · none · ref 6

    A hierarchical convolutional dictionary learning model for piecewise smooth signals using recursive scale-detail filtering and sparse coding, learned by alternating minimization and demonstrated on MNIST.