154 citations · 288 across the 9 of their papers we have counts for
9 papers
Heuristic Hyperparameter Choice for Image Anomaly Detection
Zeyu Jiang, João P. C. Bertoldo, Etienne Decencière
Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep featur…
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
Joao P. C. Bertoldo, Santiago Velasco-Forero, Jesus Angulo +1
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly…
Giga-SSL: Self-Supervised Learning for Gigapixel Images
Tristan Lazard, Marvin Lerousseau, Etienne Decencière +1
Whole slide images (WSI) are microscopy images of stained tissue slides routinely prepared for diagnosis and treatment selection in medical practice. WSI are very large (gigapixel…
[Reproducibility Report] Explainable Deep One-Class Classification
Joao P. C. Bertoldo, Etienne Decencière
Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without a…
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
João P C Bertoldo, Etienne Decencière, David Ryckelynck +1
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome,…
Dealing with Topological Information within a Fully Convolutional Neural Network
Etienne Decencière, Santiago Velasco-Forero, Fu Min +6
A fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information. A solution is proposed…
The Strong Gravitational Lens Finding Challenge
R. Benton Metcalf, M. Meneghetti, Camille Avestruz +33
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare…
Deep learning for galaxy surface brightness profile fitting
D. Tuccillo, M. Huertas-Company, E. Decencière +3
Numerous ongoing and future large area surveys (e.g. DES, EUCLID, LSST, WFIRST), will increase by several orders of magnitude the volume of data that can be exploited for galaxy mo…
Deep learning for studies of galaxy morphology
D. Tuccillo, M. Huertas-Company, E. Decenciere +1
Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the W…