DiCoDiLe: Distributed Convolutional Dictionary Learning

Abstract : Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to represent signals or images. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. Contrarily to standard patch-based dictionary learning, patterns estimated by CDL can be positioned anywhere in signals or images. Optimization techniques consequently face the difficulty of working with extremely large inputs with millions of pixels or time samples. To address this optimization problem, we propose a distributed and asynchronous algorithm, employing locally greedy coordinate descent and a soft-locking mechanism that does not require a central server. Computation can be distributed on a number of workers which scales linearly with the size of the data. The parallel computation accelerates the parameter estimation and the distributed setting allows our algorithm be used with data that does not fit into a single computer's RAM. Experiments confirm the theoretical scaling properties of the algorithm, allowing us to learn patterns on images from the Hubble Space Telescope containing tens of millions of pixels.
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Contributor : Thomas Moreau <>
Submitted on : Wednesday, November 20, 2019 - 9:23:04 AM
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  • HAL Id : hal-02371715, version 1


Thomas Moreau, Alexandre Gramfort. DiCoDiLe: Distributed Convolutional Dictionary Learning. 2019. ⟨hal-02371715⟩



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