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://github.com/akakream/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/tburgert/multi_label_noise) and [noisifier](https://github.com/akakream/noisifier), to introduce label noise to image datasets.
## 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.
## 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.
[noisifier](https://github.com/akakream/noisifier) allows you to add noise to the labels of your dataset. You can use the noisified dataset in your experiments, also to train your machine learning model robustly against label noise. Your dataset can be single label or multi-label; just create the right type of noisifier and keep adding noise.
## 2) noisifier
[noisifier](https://github.com/akakream/noisifier) allows to add noise to the labels of a dataset. The dataset can be single label or multi-label.
