Search Results for author:
Found 7 papers, 4 papers with code
Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems
When Deep Neural Networks (DNNs) are used in safety-critical systems, engineers should determine the safety risks associated with failures (i. e., erroneous outputs) observed during testing.
Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes
Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning.
Autoencoder Attractors for Uncertainty Estimation
While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features.
Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification
Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training.
Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)
In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search.
Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety.
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark
We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e. g. identical backgrounds and textures, few instances per class).
