Essentials for Class Incremental Learning

1 code implementation • 18 Feb 2021 • Sudhanshu Mittal, Silvio Galesso, Thomas Brox

Contemporary neural networks are limited in their ability to learn from evolving streams of training data.

Class Incremental Learning Incremental Learning

Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow

1 code implementation • ECCV 2018 • Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox

Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.

Optical Flow Estimation

Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection

1 code implementation • 12 Nov 2022 • Silvio Galesso, Max Argus, Thomas Brox

In this paper, we show that nearest-neighbor approaches also yield state-of-the-art results on dense novelty detection in complex driving scenes when working with an appropriate feature representation.

Anomaly Detection Density Estimation +4

Probing Contextual Diversity for Dense Out-of-Distribution Detection

1 code implementation • 30 Aug 2022 • Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar, Thomas Brox

Detection of out-of-distribution (OoD) samples in the context of image classification has recently become an area of interest and active study, along with the topic of uncertainty estimation, to which it is closely related.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +2

Compositional Servoing by Recombining Demonstrations

no code implementations • 6 Oct 2023 • Max Argus, Abhijeet Nayak, Martin Büchner, Silvio Galesso, Abhinav Valada, Thomas Brox

In this work, we present a framework that formulates the visual servoing task as graph traversal.