241 citations · 615 across the 38 of their papers we have counts for
9 papers · 1 filter
Non asymptotic bounds in asynchronous sum-weight gossip protocols
David Picard, Jérôme Fellus, Stéphane Garnier
This paper focuses on non-asymptotic diffusion time in asynchronous gossip protocols. Asynchronous gossip protocols are designed to perform distributed computation in a network of…
EdiBERT, a generative model for image editing
Thibaut Issenhuth, Ugo Tanielian, Jérémie Mary +1
Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks. However, a specialized model is often d…
Latent reweighting, an almost free improvement for GANs
Thibaut Issenhuth, Ugo Tanielian, David Picard +1
Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting different classes of images. In particu…
Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision
David Picard
In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (…
Image Collation: Matching illustrations in manuscripts
Ryad Kaoua, Xi Shen, Alexandra Durr +3
Illustrations are an essential transmission instrument. For an historian, the first step in studying their evolution in a corpus of similar manuscripts is to identify which ones co…
Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation
Victor Besnier, Andrei Bursuc, David Picard +1
In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate…