48 citations · 53 across the 6 of their papers we have counts for
10 papers
Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination
Eric Nyiri, Olivier Gibaru
Machine learning methods are solving very successfully a plethora of tasks, but they have the disadvantage of not providing any information about their decision. Consequently, esti…
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Joris Guerin, Stephane Thiery, Eric Nyiri +2
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pr…
Semantically Meaningful View Selection
Joris Guérin, Olivier Gibaru, Eric Nyiri +2
An understanding of the nature of objects could help robots to solve both high-level abstract tasks and improve performance at lower-level concrete tasks. Although deep learning ha…
Unsupervised robotic sorting: Towards autonomous decision making robots
Joris Guérin, Stéphane Thiery, Eric Nyiri +1
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object…
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Joris Guérin, Olivier Gibaru, Eric Nyiri +1
Unlike classification, position labels cannot be assigned manually by humans. For this reason, generating supervision for precise object localization is a hard task. This paper det…
CNN features are also great at unsupervised classification
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the…