Few Labels Learning

In the context of the implementation of »meta-learning strategies« among others in the »field of imaging medicine« methods like Few-Shot or Transfer-Learning are developed and researched.

Especially in the field of tissue classification in medicine, the annotation of large data sets is particularly difficult and the data situation often varies from hospital to hospital or from imaging device to imaging device. Therefore, methods of Few-Shot Learning, such as Prototypical Network, are suitable here to generalize between different applications. Transfer learning methods, whereby models are pre-trained on comparable data sets with many annotated data points, in order to then apply them to the actual problem, are also used in this context for the practical evaluation of medical CTs.

Overall, the application »Robust AI for Digital Pathology« uses methods of Few-Shot Learning for interactive tissue classification. Due to the possibility to interact with the models, possible new tissue classes can also be considered.

In the application »AI Framework for Autonomous Systems«, meta-learning strategies for autonomous driving are being researched. Reinforcement learning models are pre-trained in a simulation environment and then adapted for real-world application via transfer learning. In another part of this project, continuous learning is used to train models that can quickly and flexibly adapt to emerging scenarios in autonomous driving.

Semi-supervised learning methods are applied in scenarios where a lot of training data is available, but only a minority of it is annotated. These methods also incorporate latent information from the non-annotated data, such as similarities, into the model training in order to train high-performance machine learning models.

In a subproject, methods from the field of »Consistency Regularization« are researched and developed for the application on sequential sensor data. Furthermore, semi-supervised learning strategies are used in a project in the area of camera-based automated garbage sorting to circumvent the problem of less annotated training data.  

The annotation of training data is technically challenging and time-consuming in the area of localization. Becaue of that it is important to develop the right methods to support data mining.
In the application »Data-Driven Localization«, a measurement platform is developed for efficient data annotation using Active Learning and incorporating Predictive Uncertainty in order to suggest measurement points to the user that are expected to yield the greatest information gain.
On the other hand, the generation of the measurement or training data during data-driven localization also requires the inclusion of the statistical distribution or non-linearities of the signal propagation in the environment. Therefore, already during the data annotation phase, attention is paid to a non-uniform distribution of the class sets.
By methods like »SMOTE«, where either training data of the overrepresented class are discarded or additional data from the underrepresented class are augmented, such effects should be compensated.