Oriel Frigo — accepted AI-conference papers

2020

Iterative energy-based projection on a normal data manifold for anomaly localization

David Dehaene, Oriel Frigo, Sébastien Combrexelle, Pierre Eline

ICLR 2020poster

Autoencoder reconstructions are widely used for the task of unsupervised anomaly localization. Indeed, an autoencoder trained on normal data is expected to only be able to reconstruct normal features of the data, allowing the segmentation of anomalous pixels in an image via a simple comparison betwe…

Cited by 213 Source ↗Scholar ↗

2016

Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer

Oriel Frigo, Neus Sabater, Julie Delon, Pierre Hellier

CVPR 2016poster

This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method base…

Cited by 150 PDF ↗Scholar ↗