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.
Methods
CHIA implements:
- One-Hot Classifier as a baseline.
- Probabilistic Hierarchical Classifier Brust, C. A., & Denzler, J. (2019, November). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition (ACPR) (pp. 3-16). Springer, Cham.
- CHILLAX Brust, C. A., Barz, B., & Denzler, J. (2021, January). Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 6866-6873). IEEE.
- Self-Supervised CHILLAX Brust, C. A., Barz, B., & Denzler, J. (2021, April). Self-Supervised Learning from Semantically Imprecise Data. arXiv preprint arXiv:2104.10901.
- Semantic Label Sharing Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010, September). Semantic label sharing for learning with many categories. In European Conference on Computer Vision (pp. 762-775). Springer, Berlin, Heidelberg.
Datasets
The following datasets are integrated into CHIA:
- CORe50
- CUB200-2011
- (i)CIFAR-100
- ImageNet ILSVRC2012
- NABirds
Requirements
CHIA depends on:
- python-configuration == 0.7.1
- nltk ~= 3.5
- imageio ~= 2.6
- pillow ~= 8.0
- gputil ~= 1.4.0
- networkx ~= 2.4
- numpy ~= 1.19.2
- tensorflow-addons == 0.14.0
- tensorflow == 2.6.0
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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