Deep Learning Uncertainty using Self-Supervised Learning
Title: Deep Learning Uncertainty using Self-Supervised Learning
DNr: Berzelius-2022-228
Project Type: LiU Berzelius
Principal Investigator: Per Sidén <>
Affiliation: Linköpings universitet
Duration: 2022-12-01 – 2023-06-01
Classification: 10106


Deep learning uncertainty has been shown to greatly benefit from distance awareness. Spectral-normalized Neural Gaussian Process (SNGP, [1]) demonstrated that a deep neural network with distance awareness through spectral normalization combined with a distance-aware Gaussian process output layer can lead to high-quality uncertainty estimates using a single deterministic network. This could have large implications for safety-critical real-time applications in need of robust uncertainty predictions such as e.g. autonomous vehicles. However, the theoretical foundation for distance-awareness via spectral normalization has some flaws, and in this project we instead propose to investigate the potential of achieving distance awareness through self-supervised learning methods. [1] Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (