Now showing items 1-10 of 24
Deep Quality-informed Score Normalization for Privacy-friendly Speaker Recognition in unconstrained Environments
In scenarios that are ambitious to protect sensitive data in compliance with privacy regulations, conventional score normalization utilizing large proportions of speaker cohort data is not feasible for existing technology, since the entire cohort data would need to be stored on each mobile device. Hence, in this work we ...
Multi-scale facial scanning via spatial LSTM for latent facial feature representation
In the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation ...
Training a deep policy gradient-based neural network with asynchronous learners on a simulated robotic problem
Recent advances in deep reinforcement learning methods have attracted a lot of attention, because of their ability to use raw signals such as video streams as inputs, instead of pre-processed state variables. However, the most popular methods (value-based methods, e.g. deep Q-networks) focus on discrete action spaces ...
Deep Convolutional Neural Networks for Pose Estimation in Image-Graphics Search
Deep Convolutional Neural Networks (CNNs) have recently been highly successful in various image understanding tasks, ranging from object category recognition over image classification to scene segmentation. We employ CNNs for pose estimation in a cross-modal retrieval system, which -given a photo of an object -allows ...
Reranking-based Recommender System with Deep Learning
An enormous volume of scientific content is published every year. The amount exceeds by far what a scientist can read in her entire life. In order to address this problem, we have developed and empirically evaluated a recommender system for scientific papers based on Twitter postings. In this paper, we improve on the ...
Research Challenges for a Future-Proof E/E Architecture - A Project Statement
During the last decades, the functional power and complexity of automotive E/E architectures grew radically and is going to grow further. We identified two key factors namely autonomy and intelligence. Both pose research challenges for the next generation E/E architecture. We aim to tackle the design challenges with ...
Periocular Recognition Using CNN Features Off-the-Shelf
BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale ...
Hyper-Parameter Search for Convolutional Neural Networks - An Evolutionary Approach
SKILL 2018 - Studierendenkonferenz Informatik
Convolutional neural networks is one of the most popular neural network classes within the deep learning research area. Due to their specific architecture they are widely used to solve such challenging tasks as image and speech recognition, video analysis etc. The architecture itself is defined by a number of (hyper-)parameters ...
Adversarial Attacks on Graph Neural Networks
INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft
Proof of Concept: Automatic Type Recognition
The type used to print an early modern book can give scholars valuable information about the time and place of its production as well as its producer. Recognizing such type is currently done manually using both the character shapes of 'M' or 'Qu' and the size of the total type to look it up in a large reference work. ...