Now showing items 1-6 of 6
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 ...