This repository contains links to two tools, namely [multi-label noise injection](https://gitlab.tubit.tu-berlin.de/tburgert/multi_label_noise) and [noisifier](https://gitlab.tubit.tu-berlin.de/rsim/noisifier), which both help to inject label noise into image datasets. The resulting noisified dataset can be used in experiments to train machine learning models robust against label noise. Both tools work on [Numpy](https://numpy.org/) arrays and are independent of specific deep learning libraries.
This repository contains links to two tools, namely [multi-label noise injection](https://gitlab.tubit.tu-berlin.de/rsim/multi_label_noise) and [noisifier](https://gitlab.tubit.tu-berlin.de/rsim/noisifier), which both help to inject label noise into image datasets. The resulting noisified dataset can be used in experiments to train machine learning models robust against label noise. Both tools work on [Numpy](https://numpy.org/) arrays and are independent of specific deep learning libraries.
## 1) multi-label noise injection
[multi-label noise injection](https://gitlab.tubit.tu-berlin.de/tburgert/multi_label_noise) contains a set of helper functions in order to create noisy multi-label matrices. The tool allows to inject additive (extra classes) and subtractive (missing classes) noise separately.
[multi-label noise injection](https://gitlab.tubit.tu-berlin.de/rsim/multi_label_noise) contains a set of helper functions in order to create noisy multi-label matrices. The tool allows to inject additive (extra classes) and subtractive (missing classes) noise separately.
