The aim of the project is to develop methods that allow AI systems to deal with changing conditions efficiently - without necessary changes in the architecture and without requiring a completely new training. The approach of continuous learning is applied: Here, the learned knowledge is not forgotten when transferring from one domain to another. Furthermore, methods are developed that can handle several domains at the same time.
As a basis for the consideration of concrete Delta Learning-related use cases a broad collection of methods of continuous learning is developed and investigated. These primarily address situations where comparatively little new data is added to a still relevant database. Traditional approaches lead to "catastrophic forgetting": the networks unlearn how to handle the original, already well mastered database. This problem is addressed by investigating countermeasures, including the study of intermediate representations and continuous training.
Use of synthetic data
Synthetic data from simulation environments promise a favourable acquisition of large amounts of annotated training examples and an explicit control over the scenarios depicted in them. On the one hand, this enables the investigation of scenarios that rarely occur in the real world or that endanger human life. On the other hand, the accurate control over data generation allows the precise, isolated examination of individual deltas. Despite the progress of modern rendering engines, they are not photorealistic enough to transfer learned models from the simulation, the source domain, to the real world, the target domain. For this reason, methods to overcome the delta between synthetic and real data are analysed.
Cross sensor adaptation
In the field of cross sensor adaptation, sensor-specific cases of domain adaptation, i.e. deltas with respect to their different sensor configurations, are investigated. In addition to changes in sensor type and sensor technology, parameters such as resolution, opening angle and installation position must be taken into account for the deltas to be bridged. Methods that have been identified as suitable for closing the deltas are meta-learning methods, methods that use invariant features, GAN methods, processes for physical transformation and knowledge graph methods.
Location and time domain
Changes in the location domain as well as long-term changes in the time domain currently pose a great challenge for AI modules trained on other data. Examples are changes of the seasons or country-specific appearances, such as different road markings or signage. Often AI modules have to be re-trained for unknown or different looking locations. The project explores the efficient handling of these changes and develops methods for delta learning and knowledge transfer.
In addition to long-term temporal changes, short-term changes in the environment, for example due to weather conditions or daytime, will also be investigated. Here the adaptation of AI modules to different dynamic changes of the same environment is examined without having recorded all changes for each type of environment.