Search Results for author:
Found 21 papers, 5 papers with code
What should AI see? Using the Public's Opinion to Determine the Perception of an AI
Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.
Tailored Uncertainty Estimation for Deep Learning Systems
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable.
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data.
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception.
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates
In this paper, we present a time-dynamic approach to model uncertainties of instance segmentation networks and apply this to the detection of false positives as well as the estimation of prediction quality.
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs
Deep neural networks are often not robust to semantically-irrelevant changes in the input.
From a Fourier-Domain Perspective on Adversarial Examples to a Wiener Filter Defense for Semantic Segmentation
Despite recent advancements, deep neural networks are not robust against adversarial perturbations.
Risk Assessment for Machine Learning Models
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment.
Self-Supervised Domain Mismatch Estimation for Autonomous Perception
Autonomous driving requires self awareness of its perception functions.
Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
Safety is one of the most important development goals for highly automated driving (HAD) systems.
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation
We present proof-of-concept results for CIFAR-10, and prove the efficiency of our method for the semantic segmentation of street scenes on the Cityscapes dataset based on predicted instances of the 'human' class.
Detection of False Positive and False Negative Samples in Semantic Segmentation
In recent years, deep learning methods have outperformed other methods in image recognition.
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation
Neural networks for semantic segmentation can be seen as statistical models that provide for each pixel of one image a probability distribution on predefined classes.
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks
We call this the "attack generator".
GAN- vs. JPEG2000 Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems.
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
We approach such potential misclassifications by weighting the posterior class probabilities with the prior class probabilities which in our case are the inverse frequencies of the corresponding classes in the training dataset.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
We aggregate these dispersion measures segment-wise and derive metrics that are well-correlated with the segment-wise IoU of prediction and ground truth.
Introducing Noise in Decentralized Training of Neural Networks
It has been shown that injecting noise into the neural network weights during the training process leads to a better generalization of the resulting model.
Efficient Decentralized Deep Learning by Dynamic Model Averaging
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.
