Modular Concept Learning

Our Modular Concept Learning helps companies with image classification tasks to develop explainable and easily adaptable Artificial Intelligence (AI) models for image classification by breaking up a complex classification task into subtasks of recognizing  relevant visual concepts, and combining these concepts into a transparent  decision process. Unlike other AI models in image classification, our Modular  Concept Learning increases not only explainability but also adaptability to changes in the task or data through its modularity and reusability of concept models.

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Challenge: Explaining the Model's Decision-Making

Most state-of-the-art Machine Learning (ML) models treat the classification of an image as one complex task. Not only does this often require costly and time-consuming retraining of the entire ML model in the case of changes to the task or data, but also does it make the model’s decision-making process difficult to explain. A lack in explainability reduces trust in the model’s decision (output), which in turn makes a model less likely to be used in a safety-critical context, e.g., medical imaging.

Solution: Breaking up Image Classification into Subtasks

Instead of training an ML model directly on the entire task (e.g., traffic sign classification), we guide the model to follow a process like how humans would classify an image: They would usually look for the presence or absence of certain features or concepts in an image (e.g., shape, color, icon on the traffic  sign) to make their decision. In a similar manner, our Modular Concept Learning breaks up an image classification task into subtasks (concept models of e.g., shape, texture, color) and combines the detected concepts into an overall  classification decision.

The mapping of concepts (e.g., color of the traffic sign) to the right class (red = stop/no entry sign) can either be manually modelled by a domain expert or trained on data. If the task or data changes after deployment, retraining can be limited to one or few concepts of the model instead of the entire model. In addition, it is easier to explain the cause of a wrong classification (e.g., the color was misclassified but the shape and icon were classified correctly), reducing both training and error investigation efforts.  


  • Interpretability: The ML model’s decision is explained using the detected concepts and their relationship to each other.
  • Flexibility: The building blocks can be customized to the specific requirements of the use case (e.g., a combination of classical algorithms and AI models).
  • Adaptability: Rather than retraining the entire model, our approach allows  for targeted adaptation of parts of the model to respond to changes in the task and/or dataset.
  • Error Analysis: The modularity enables a more detailed error analysis, e.g.,   which concept is difficult to detect, and the application of more targeted   measures, e.g., collecting more data of a specific concept.

Our other core competencies

In addition to Modular Concept Learning, we also focus on the following topics:

Person and Object Detection

Robust AI: Uncertainty Estimation


Industrial Sensors

The automation of production requires reliable systems for the real-time monitoring and control of processes. Visit Industrial Sensors to learn more about our focus areas and the various projects Fraunhofer IKS is working on.

FAST - Feedback-guided Automation of Sub-tasks

Standards for AI, Safety and Automation