# Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals

## TL;DR

This paper introduces DiCoDiLe, a distributed and asynchronous convolutional dictionary learning algorithm that efficiently discovers patterns in large-scale images and signals, demonstrated on Hubble Space Telescope images.

## Contribution

It presents a novel distributed, asynchronous optimization algorithm for convolutional dictionary learning that scales linearly with data size, enabling pattern discovery in very large images.

## Key findings

- Algorithm scales linearly with data size
- Effective pattern discovery on large images
- Validated on Hubble Space Telescope images

## Abstract

Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. As estimated patterns can be positioned anywhere in signals or images, optimization techniques face the difficulty of working in extremely high dimensions with millions of pixels or time samples, contrarily to standard patch-based dictionary learning. To address this optimization problem, this work proposes a distributed and asynchronous algorithm, employing locally greedy coordinate descent and an asynchronous locking mechanism that does not require a central server. This algorithm can be used to distribute the computation on a number of workers which scales linearly with the encoded signal's size. Experiments confirm the scaling properties which allows us to learn patterns on large scales images from the Hubble Space Telescope.