Job Call for Phd or PostDoc position in AutoML. We are looking for a new team member! Please share if you know someone who could be interested.
Paper accepted at MIC 2020:
Using AutoML to Optimize Shape Error Prediction in Milling Processes. We show how AutoML can be used in miling processes to substantially improve the predicted error with little effort within minutes.
Paper accepted to WACV 2021:
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows. We propose DifferNet: It estimates a probability distribution of image features of non-defective components to detect defective examples. Furthermore, the defective areas can be localized. Our method does not require defective examples in training.
Code is available on Github.
Paper accepted to AIIDE 2020:
TOAD-GAN: Coherent Style Level Generation from a Single Example.
This paper has received the Best Student Paper Award.
We present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that generates token-based video game levels.
Check out our Code released on Github and the Demonstrator, with which you can play our generated levels.
Journal article accepted for IEEE Transactions on Image Processing, vol. 29, 2020: Analysis of Affine Motion-Compensated Predictionin Video Coding
We are living in the era of information: sharing and sending pictures, videos and multimedia data over the network has become part of our everyday lives. This demands for information processing algorithms to encode, transmit, enhance and extract meaningful information from multimedia content. At our institute, we conduct cutting-edge research in the fields of audio, video, SAR and genome signal processing, computer vision and machine learning, incl. deep learning, reinforcement learning and automated machine learning. Broadly speaking, this involves designing intelligent algorithms to extract relevant information from data.
Humans constantly extract meaningful information from visual data almost effortlessly. It turns out that simple visual tasks such as recognizing persons, detecting and tracking objects or understanding what is going on in scenes are challenging problems for a computer. Training computers to process information as humans do has many potential applications in fields such as communication systems, medicine, artificial intelligence, robotics, surveillance, entertainment or sports science. It is therefore our ultimate goal to be able to emulate the human visual system with computational algorithms.
The “Institut für Informationsverarbeitung” (information processing) which was previously known as “Theoretische Nachrichtentechnik und Informationsverarbeitung” (tnt) was founded in 1973 by Prof. Dr.-Ing. Hans-Georg Musmann and is part of the Faculty of Electrical Engineering and Computer Science of the Gottfried Wilhelm Leibniz Universität Hannover. Today the group is headed by Prof. Dr.-Ing. Jörn Ostermann, Prof. Dr.-Ing. Bodo Rosenhahn and Prof. Dr. Marius Lindauer and consists of about 30 researchers from more than seven different nationalities. Our technology is applied to telecommunication, digital systems, automation and interpretation tasks, remote sensing or medical image analysis. Most of our research is funded by industry, national and international research grants.
Do you want to join us? We have open positions.