https://github.com/ap6yc/dccr
Deep Clustering Context Recognition (DCCR); materials for the upcoming paper "Lifelong Context Recognition via Online Deep Feature Clustering."
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.5%) to scientific vocabulary
Repository
Deep Clustering Context Recognition (DCCR); materials for the upcoming paper "Lifelong Context Recognition via Online Deep Feature Clustering."
Basic Info
- Host: GitHub
- Owner: AP6YC
- License: cc-by-4.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://ap6yc.github.io/DCCR/
- Size: 12.5 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 5
Metadata Files
README.md
DCCR
Deep Clustering Context Recognition (DCCR); materials for the upcoming TNNLS paper "Lifelong Context Recognition via Online Deep Feature Clustering." Please see the documentation.
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Table of Contents
Usage
Experiments are enumerated in src/experiments.
Each has a README.md that describes the experiment and how to run it.
Most experiments only require instantiating the Julia project in this repo with
julia
using Pkg; Pkg.activate("."); Pkg.instantiate()
and running the script in the experiment folder with either the shell command:
shell
julia scripts/1_accuracy/1_unshuffled.jl
or in an existing REPL environment with the include command:
julia
include("scripts/1_accuracy/1_unshuffled.jl")
Experiments with multiple stages or multiple interpreters (Julia, Python, and shell script) contain details for their reproducibilty.
File Structure
An explanation of the DCCR project file structure can be found in the hosted documentation.
Contributing
If you have an issue with the project, please raise an issue. If you would instead like to contribute to the package, please see the contributing guide.
Attribution
Authors
- Sasha Petrenko sap625@mst.edu
- Andrew Brna andrew.brna@teledyne.com
License
Shield: 
This work is licensed under a Creative Commons Attribution 4.0 International License.
Useful Links
The following resources are referenced in this project or are useful resources for reference:
- Avalanche docs
- Avalanche continual-learning-baselines repo
- Deep Streaming Linear Discriminant Analysis (DSLDA):
- Continual Prototype Evolution (CoPE):
Assets
The following external assets are used in this project by attribution:
Citation
This project has a citation file file that generates citation information for the package and corresponding JOSS paper, which can be accessed at the "Cite this repository button" under the "About" section of the GitHub page.
You may also cite this repository with the following BibTeX entry:
bibtex
@software{Petrenko_AP6YC_DCCR_2023,
author = {Petrenko, Sasha},
doi = {10.5281/zenodo.8017806},
month = jun,
title = {{AP6YC/DCCR}},
year = {2023}
}
Owner
- Name: Sasha Petrenko
- Login: AP6YC
- Kind: user
- Website: https://ap6yc.github.io/
- Repositories: 48
- Profile: https://github.com/AP6YC
Graduate researcher of applied computational intelligence at the Missouri University of Science and Technology.
Citation (CITATION.cff)
title: "AP6YC/DCCR"
abstract: "This software is a Julia project for Deep Clustering Context Recognition."
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
keywords:
- "ART"
- "Adaptive Resonance Theory"
- "Adaptive Resonance"
- "Clustering"
- "Lifelong Learning"
- "Lifelong Machine Learning"
authors:
- family-names: "Petrenko"
given-names: "Sasha"
orcid: https://orcid.org/0000-0003-2442-8901
website: "https://ap6yc.github.io/"
email: "sap625@mst.edu"
alias: "AP6YC"
affiliation: "Missouri University of Science and Technology"
doi: 10.5281/zenodo.8017806
date-released: 2023-06-08
url: "https://doi.org/10.5281/zenodo.8017806"
repository-code: "https://github.com/AP6YC/DCCR"
identifiers:
- description: "The DOI of the latest DCCR Zenodo archive."
type: "doi"
value: "10.5281/zenodo.8017806"
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 27 days
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
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- github-actions[bot] (2)
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Dependencies
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