Concept Hierarchies for Incremental and Active Learning
Project description
CHIA: Concept Hierarchies for Incremental and Active Learning
CHIA is a collection of methods and helper functions centered around hierarchical classification in a lifelong learning environment. It forms the basis for some of the experiments and tools developed at Computer Vision Group Jena.
Requirements
CHIA depends on:
- python-configuration == 0.7.1
- nltk ~= 3.5
- imageio ~= 2.6
- pillow ~= 7.1.0
- gputil ~= 1.4.0
- networkx ~= 2.4
- numpy ~= 1.18.5
- tensorflow-addons == 0.11.1
- tensorflow == 2.3.0
Optional dependencies:
- tables ~= 3.6.1
- pandas ~= 1.0.4
- sacred ~= 0.8.1
- pyqt5 ~= 5.15.0
- scikit-image ~= 0.17.2
- scikit-learn ~= 0.23.1
- scipy == 1.4.1
- matplotlib ~= 3.2.1
Installation
To install, simply run:
pip install chia
or clone this repository, and run:
pip install -U pip setuptools
python setup.py develop
We also include the shell script quick-venv.sh
, which creates a virtual environment and install CHIA for you.
Getting Started
To run the example experiment which makes sure that everything works, use the following command:
python examples/experiment.py examples/configuration.json
After a few minutes, the last lines of output should look like this:
[DEBUG] [ExceptionShroud]: Leaving exception shroud without exception
[SHUTDOWN] [Experiment] Successful: True
Citation
If you use CHIA for your research, kindly cite:
Brust, C. A., & Denzler, J. (2019, November). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition (pp. 3-16). Springer, Cham.
You can refer to the following BibTeX:
@inproceedings{Brust2019IDK,
author = {Clemens-Alexander Brust and Joachim Denzler},
booktitle = {Asian Conference on Pattern Recognition (ACPR)},
title = {Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers},
year = {2019},
}
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