Search Results for author:
Found 9 papers, 3 papers with code
3D Compression Using Neural Fields
Neural Fields (NFs) have gained momentum as a tool for compressing various data modalities - e. g. images and videos.
ManiFlow: Implicitly Representing Manifolds with Normalizing Flows
In contrast to prior work, we approach this problem by generating samples from the original data distribution given full knowledge about the perturbed distribution and the noise model.
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems.
Implicit Neural Representations for Image Compression
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types.
On the Practicality of Deterministic Epistemic Uncertainty
We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.
Out of Distribution (OOD) Detection
Semantic Segmentation
+1
Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction
Recently normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time.
Variational Transformer Networks for Layout Generation
Generative models able to synthesize layouts of different kinds (e. g. documents, user interfaces or furniture arrangements) are a useful tool to aid design processes and as a first step in the generation of synthetic data, among other tasks.
The Hidden Uncertainty in a Neural Networks Activations
We find that this leads to improved OOD detection of epistemic uncertainty at the cost of ambiguous calibration close to the data distribution.
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty of a neural network.
