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https://github.com/cabrust/chia
CHIA: Concept Hierarchies for Incremental and Active Learning
https://github.com/cabrust/chia
Last synced: 8 days ago
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CHIA: Concept Hierarchies for Incremental and Active Learning
- Host: GitHub
- URL: https://github.com/cabrust/chia
- Owner: cabrust
- License: bsd-3-clause
- Created: 2020-08-11T09:28:01.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-06T14:17:43.000Z (almost 3 years ago)
- Last Synced: 2024-10-31T15:48:31.260Z (13 days ago)
- Language: Python
- Size: 261 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CHIA: Concept Hierarchies for Incremental and Active Learning
CHIA implements methods 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](http://www.inf-cv.uni-jena.de/).
Development is continued at the [DLR Institute of Data Science](https://www.dlr.de/dw/en/desktopdefault.aspx/tabid-12192/21400_read-49437/)
**Methods**\
CHIA implements:
* **One-Hot Softmax Classifier** as a baseline.
* **Probabilistic Hierarchical Classifier** Brust, C. A., & Denzler, J. (2019). *Integrating domain knowledge: using hierarchies to improve deep classifiers*. In Asian Conference on Pattern Recognition (ACPR)
* **CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2021). *Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge*. In International Conference on Pattern Recognition (ICPR).
* **Self-Supervised CHILLAX** Brust, C. A., Barz, B., & Denzler, J. (2022). *Self-Supervised Learning from Semantically Imprecise Data*. In Computer Vision Theory and Applications (VISAPP)
* **Semantic Label Sharing** Fergus, R., Bernal, H., Weiss, Y., & Torralba, A. (2010). *Semantic label sharing for learning with many categories*. In European Conference on Computer Vision (ECCV).
**Datasets**\
CHIA has integrated support including hierarchies for a number of popular datasets. See [here](docs/architecture.md#dataset) for a complete list.
## Installation and Getting Started
CHIA is available on PyPI. To install, simply run:
```bash
pip install chia
```
or clone this repository, and run:
```bash
pip install -e .
```
To run the [example experiment](examples/experiment.py) which makes sure that everything works, use the following command:
```bash
python examples/experiment.py examples/configuration.json
```
After a few minutes, the last lines of output should look like this:
```text
[SHUTDOWN] [Experiment] Successful: True
```
## Documentation
The following articles explain more about CHIA:
* [Architecture](docs/architecture.md) explains the overall construction. It also includes reference descriptions of most classes.
* [Configuration](docs/configuration.md) describes how experiments and CHIA itself are configured.
* [Using your own dataset](docs/dataset.md) explains our JSON format for adding your own data.
## Citation
If you use CHIA for your research, kindly cite:
> Brust, C. A., & Denzler, J. (2019). Integrating domain knowledge: using hierarchies to improve deep classifiers. In Asian Conference on Pattern Recognition. Springer, Cham.
You can refer to the following BibTeX:
```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},
doi = {10.1007/978-3-030-41404-7_1}
}
```