no code implementations • 14 Dec 2023 • Mattijs Baert, Sam Leroux, Pieter Simoens
The alignment of autonomous agents with human values is a pivotal challenge when deploying these agents within physical environments, where safety is an important concern.
no code implementations • 4 May 2023 • Mattijs Baert, Pietro Mazzaglia, Sam Leroux, Pieter Simoens
To address this challenge, we propose a novel method that utilizes the principle of maximum causal entropy to learn constraints and an optimal policy that adheres to these constraints, using demonstrations of agents that abide by the constraints.
no code implementations • 26 Aug 2022 • Sander De Coninck, Wei-Cheng Wang, Sam Leroux, Pieter Simoens
Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance.
no code implementations • 21 Mar 2022 • Sam Leroux, Pieter Simoens, Meelis Lootus, Kartik Thakore, Akshay Sharma
Deploying machine learning applications on edge devices can bring clear benefits such as improved reliability, latency and privacy but it also introduces its own set of challenges.
no code implementations • 28 Oct 2021 • Sander De Coninck, Sam Leroux, Pieter Simoens
Crowd management relies on inspection of surveillance video either by operators or by object detection models.
no code implementations • WACV 2021 • Sam Leroux, Bo Li, Pieter Simoens
Automated anomaly detection in surveillance videos has attracted much interest as it provides a scalable alternative to manual monitoring.
Anomaly Detection In Surveillance Videos Video Anomaly Detection
no code implementations • 19 Jan 2021 • Mattijs Baert, Sam Leroux, Pieter Simoens
For this, we train a variational autoencoder on high quality face images from a publicly available dataset and use the reconstruction probability as a metric to estimate the quality of each face crop.
1 code implementation • 10 Nov 2020 • Bo Li, Sam Leroux, Pieter Simoens
Automating the analysis of surveillance video footage is of great interest when urban environments or industrial sites are monitored by a large number of cameras.
no code implementations • 17 Apr 2019 • Ozan Çatal, Johannes Nauta, Tim Verbelen, Pieter Simoens, Bart Dhoedt
Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task.
no code implementations • 11 Sep 2018 • Pieter Van Molle, Miguel De Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
In this paper, we try to open the black box of the CNN by inspecting and visualizing the learned feature maps, in the field of dermatology.
no code implementations • 9 Jun 2018 • Pieter Van Molle, Tim Verbelen, Elias De Coninck, Cedric De Boom, Pieter Simoens, Bart Dhoedt
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.
no code implementations • 30 May 2018 • Sam Leroux, Tim Verbelen, Pieter Simoens, Bart Dhoedt
Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices.
no code implementations • 26 Apr 2018 • Sam Leroux, Pavlo Molchanov, Pieter Simoens, Bart Dhoedt, Thomas Breuel, Jan Kautz
Deep residual networks (ResNets) made a recent breakthrough in deep learning.
no code implementations • 29 Nov 2017 • Sam Leroux, Steven Bohez, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
Binary neural networks are attractive in this case because the logical operations are very fast and efficient when implemented in hardware.
no code implementations • 9 Aug 2017 • Pieter Van Molle, Tim Verbelen, Steven Bohez, Sam Leroux, Pieter Simoens, Bart Dhoedt
However, when learning a task using reinforcement learning, the agent cannot distinguish the characteristics of the environment from those of the task.
no code implementations • 13 Mar 2017 • Steven Bohez, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy.
no code implementations • 27 May 2016 • Sam Leroux, Steven Bohez, Cedric De Boom, Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.
1 code implementation • 9 May 2016 • Cedric De Boom, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester, Bart Dhoedt
We present four training and prediction schedules from the same character-level recurrent neural network.