IEEE

Convolutional Sparse and Low-Rank Coding-Based Image Decomposition

Abstract: We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently ...
EurekAlert!

Quasi-convolution coding empowers streamlined reservoir computer

A new publication from Opto-Electronic Sciences; DOI 10.29026/oea.2025.240135, discusses how quasi-convolution coding empowers a streamlined reservoir computer. As the pace of digitalization ...
Embedded

Understanding convolutional neural networks

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
IEEE

Efficient ADMM-Based Algorithms for Convolutional Sparse Coding

Abstract: Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on ...
Frontiers

Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks

Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation. However, it remains ...
syncedreview

A Guide to Receptive Field Arithmetic for Convolutional Neural Networks

The receptive field is defined as the region in the input space that a particular CNN’s feature is looking at (i.e. be affected by). For convolutional neural network, the number of output features in ...