P
Paolo Favaro
Researcher at University of Bern
Publications - 163
Citations - 11240
Paolo Favaro is an academic researcher from University of Bern. The author has contributed to research in topics: Image restoration & Deblurring. The author has an hindex of 44, co-authored 150 publications receiving 8666 citations. Previous affiliations of Paolo Favaro include University of California, Los Angeles & University of Edinburgh.
Papers
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Book ChapterDOI
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi,Paolo Favaro +1 more
TL;DR: In this article, a siamese-ennead convolutional neural network (CFN) is proposed to build features suitable for object detection and classification without human annotation and later transferred via fine-tuning on a different, smaller and labeled dataset.
Posted Content
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi,Paolo Favaro +1 more
TL;DR: A novel unsupervised learning approach to build features suitable for object detection and classification and to facilitate the transfer of features to other tasks, the context-free network (CFN), a siamese-ennead convolutional neural network is introduced.
Journal ArticleDOI
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution
Tom E. Bishop,Paolo Favaro +1 more
TL;DR: This work addresses traditional multiview stereo methods to the extracted low-resolution views can result in reconstruction errors due to aliasing, and incorporates Lambertian and texture preserving priors to reconstruct both scene depth and its superresolved texture in a variational Bayesian framework.
Journal ArticleDOI
Low rank subspace clustering (LRSC)
René Vidal,Paolo Favaro +1 more
TL;DR: This work poses the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaced and corrupted by noise and/or gross errors as a non-convex optimization problem, and solves the problem using an alternating minimization approach.
Proceedings ArticleDOI
Representation Learning by Learning to Count
TL;DR: This paper uses two image transformations in the context of counting: scaling and tiling to train a neural network with a contrastive loss that produces representations that perform on par or exceed the state of the art in transfer learning benchmarks.