Deep Learning

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).

Head

Prof. Dr. Prof. h.c. Andreas Dengel
E-mail:
Phone: +49 631 20575-1000

Contact

German Research Center for Artificial Intelligence GmbH, DFKI
Trippstadter Straße 122
67663 Kaiserslautern
Germany

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz