Deep Learning Competence Center

German Research Center for Artificial Intelligence (DFKI)


The Competence Center for Deep Learning of the DFKI focuses on:

Our research is based on deep learning and machine learning algorithms and ranges from basic research to industrial knowledge transfer. In this context we employ deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) approaches to advance machine perception, work with deep learning frameworks such as CAFFE and Torch, collaborate with academic institutes in this area, and teach these approaches to build a new generation of students embracing machine learning. Our work is funded through public national and European research grants and direct industrial contracts.

Our expertise is centered around Deep Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bi-directional Recurrent Neural Networks, Convolutional Deep Belief Networks, Deep Boltzmann Machines, Autoencoders, Deep Reinforcement Learning, Support Vector Machines (SVM), Vector Quantization (VQ), Energy Functional Minimization, RANSAC - based optimization, Kernel Methods, k-Nearest Neighbor (kNN), Gaussian Mixture Models (GMM), Conditional Random Fields (CRF), Max Entropy, Hidden Markov Models (HMM), Case-base Reasoning (CBR).

research projects


Tasty food, sweet animals, breathtaking landscapes - to characterize something briefly, an adjective together with a noun is being used. These adjective noun pairs (ANP) describe the visual content of an image together with the feelings that it triggers at the viewer. If they occur with great frequency, they can be used for the machine description of images that go beyond a textual reproduction of the visual content. Capttitude (Captions with Attitude) is a system able to produce affective captions with an emotional component. Two different methods are used, which as a result provide two variants of a text caption with emotional content; a Convolutional Neural Network (CNN) together with a long-short-term-memory (LSTM) network, and in the second approach a graph-based concept and syntax transition (CAST) network.


The project DeepEye faces the challenge of recognizing natural catastrophes in satellite images and to enrich the information with multi-media content gained from the social media. With an image analysis, the different spectral bands of satellite data are combined and the geographic areas affected by a natural catastrophe will be extract. In a multimodal analysis, relevant information from text, image and meta data is extracted using various methods of machine learning such as Convolutional Neural Networks. The focus of the analysis is the extraction of contextual aspects in order to obtain a comprehensive and complete view of a specific event. These prepared data can be used in the context of crisis management, for example for the coordination of the rescue forces in situ. With the combination of satellite data and multi-media content from social media, DeepEye aims for the next big step in crisis management: detailed depictions of natural catastrophes by the fusion of different information channels.


The TRACTAT project aims to lay the foundation for a smooth and effective Transfer of Control (ToC) between autonomous systems and humans in cyber-physical environments. In case of self-driving vehicles, the system must occasionally ask a human to take over in anomalous or otherwise unexpected situations. We train our ML systems based on a variety of user, environment, and situation related data ranging from traffic information to biosensor input to predict the smallest feasible time of transfer to allow a safe takeover.

Project Skincare

In Skincare, we develop a mobile application for patients and health professionals in the context of skin cancer diagnosis and treatment. We will combine patient records with mobile images for knowledge discovery and knowledge acquisition toward decision support and services in clinical and non-clinical environments. Input modes include smartphones for a direct digitization of patient data and images. The innovative aspect is a holistic view on individual patients based on teledermatology, whereby patient data and lesions photographed with a mobile device can be taken into account for clinical and non-clinical decision support.

Project ERICS

Refugees and migrants often lack essential information about asylum, housing, health, kindergarten, university, or work. Even when they know where and how to query this information on the Internet, this process can be very time-consuming, exhausting, disorienting, and confusing. How does ERICS and the Eike chatbot based on deep learming help? We have built an easy-to-use chatbot service that is able to interact with refugees and migrants via natural language. In order to do so, we designed a new user interface to: create an empathic and engaging visual interface; facilitate a natural text-based dialogue between users and our chatbot during the information seeking and retrieval process; collect user feedback to further improve the chatbot service across time using machine learning.


To allow for better integration of renewable energy into our energy systems, it is essential to forecast the amount of energy production in order to keep the grids stable or to avoid blackouts. In our energy projects (Designetz, charge4C, BloGPV, PolyEnergyNet) we provide deep learning techniques for day-ahead and intraday forecasting of photovoltaic and wind energy production as well as forecasting the electricity consumption of private households. We use weather forecasts and historical production data to build and train the corresponding prediction models. Learning techniques for optimizing energy storage management are also investigated.


Predictive maintenance for printers will bring a shift in the performance and durability of commercial printing equipment. In the current commercial printing environment, commercial printers need to be able to deliver constant uptime at the customer location. To avoid service interruptions, manufacturers have mainly emphasized corrective and preventive maintenance. Corrective maintenance is about fixing a machine when it really is broken, while preventive maintenance involves replace parts when they are about to exceed their expected lifespan for error-free service. However, these two approaches do not satisfy the growing need of commercial printing services for reliability and predictability in their production systems.
Océ, together with technology provider DFKI, is taking the path of focusing on predicting, rather than correcting or preventing potential issues. Using sensor data produced by commercial printer, and analyzing this with an algorithm, makes it possible to determine when a part, or multiple parts, are starting to fail, so corrective actions can be taken. The algorithm itself is being developed by DFKI based on a large set of different data coming from different printers and spanning several months of operations.
A first working prototype of the whole system is showcased by the end of 2018 and a matured product will be expected at the end of 2019.


Prof. Dr. Prof. h.c. Andreas Dengel


Dr. Stefan

Senior Researcher

Dr.-Ing. Sheraz Ahmed

Senior Researcher

Dr. Jörg

Senior Researcher

Dr. Boris

Senior Researcher

Dr.-Ing. Saqib Bukhari

Senior Researcher

Dr. Matthieu

Senior Researcher

Dr. Ahmed

Senior Researcher

Dr.-Ing. Michael Feld

Senior Researcher

Dr. Peter

Senior Researcher

Dr. Jörn

Senior Researcher

Dr. Georg

Senior Researcher

Dr. Alexis

Senior Researcher

Dr. Kim

Senior Researcher

Dr. Elsa

Senior Researcher

Dr. Raphael


Dr. Christian Schulze

Senior Researcher

Dr. Jingyi


Contact Us

Prof. Andreas Dengel
German Research Center for Artificial Intelligence
Trippstadter Straße 122
67663 Kaiserslautern

Phone: (+49 631 20575 1000)