Tag: dictionary learning

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Released TanSacNet Pre-release20250105

We updated TanSacNet Project for developing tangent space adaptive control networks.

  • New PyTorch Implementation: Introduced a PyTorch-based 2-D LSUN framework, including batch processing in orthonormalTransform.py and a low-dimensional approximation sample. Optimized gradient computation and sequential matrix processing to improve efficiency in PyTorch and CUDA environments.
  • Enhanced MATLAB Support: Improved data type and device management to ensure stability and performance in MATLAB workflows.
  • Code Refactoring and Stability: Streamlined code structure for maintainability and resolved initialization and CPU processing issues across both frameworks.

This pre-release highlights the new PyTorch implementation, along with key updates to MATLAB support and overall code stability.

Paper Published in IAPSIPA Transactions on Signal and Information Processing

 

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Published SaivDr-Release20200903

We updated SaivDr (Sparsity-Aware Image and Volumetric Data Restoration) package for the first time in about six months.

New this time, we added custom layers and sample codes for use with MATLAB Deep Learning Toolbox. It allows for more flexible DAG configuration than before.

NSOLT enables you to realize Parseval tight, symmetric and multi-resolution convolutional layers, and you can place NSOLT as a convolutional layer in a corner of a convolutional neural network.

We hope you will give it a try.

Acknowledgments: This work was supported by KAKENHI JP19H04135.

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Accepted by IEEE/IEIE ICCE-Asia 2020

The following research topic has been accepted by IEEE/IEIE ICCE-Asia @Seoul, Republic of Korea

Title: ‘Convolutional Nonlinear Dictionary with Cascaded Structure Filter Banks’
Authors: Ruiki Kobayashi and Shogo Muramatsu

The following tutorial is also planed.

Tutorial Title: Sparsity-Aware High-Dimensional Data Restoration with Convolutional Dictionary Learning
Lecturer: Shogo Muramatsu

The event was postponed from April to November due to the impact of COVID-19.

 

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Accepted by IEEE ICASSP 2019

The following work was accepted by IEEE ICASSP2019@Brighton (UK).

Title: ‘CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION’
Authors: Yuhei Kaneko, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka, Yu Otake, Shunsuke Ono, Masahiro Yukawa
Session Type: Poster
Session Title: ‘Enhancement and Restoration II’

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Citation of NSOLT paper in ICASSP2014

Our paper on NSOLT (ICASSP 2014) was cited as a method of structured dictionary learning in the following paper by Prof. Kjersti Engan (University of Stavanger, Norway) famous for the method of optimal directions (MOD).

About NSOLT, please visit here.