Search Results for author: Lukas Jendele

Found 4 papers, 2 papers with code

Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning

no code implementations14 Feb 2020 Sammy Christen, Lukas Jendele, Emre Aksan, Otmar Hilliges

We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios.

Continuous Control Decision Making +2

Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks

no code implementations25 Sep 2019 Lukas Jendele, Sammy Christen, Emre Aksan, Otmar Hilliges

Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.

Continuous Control Decision Making +3

Adversarial Augmentation for Enhancing Classification of Mammography Images

1 code implementation20 Feb 2019 Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu

Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging.

Classification General Classification +2

Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks

2 code implementations19 Nov 2018 Anton S. Becker, Lukas Jendele, Ondrej Skopek, Nicole Berger, Soleen Ghafoor, Magda Marcon, Ender Konukoglu

At the higher resolution, all radiologists showed significantly lower detection rate of cancer in the modified images (0. 77-0. 84 vs. 0. 59-0. 69, p=0. 008), however, they were now able to reliably detect modified images due to better visibility of artifacts (0. 92, 0. 92 and 0. 97).

Adversarial Attack

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