182 citations
- Technicolor (United States)US18 papers
- Technicolor (France)FR14 papers
- Institut national de recherche en sciences et technologies du numériqueFR6 papers
- Stanford UniversityUS5 papers
- École Polytechnique Fédérale de LausanneCH3 papers
- Max Planck Institute for InformaticsDE3 papers
- Rutgers, The State University of New JerseyUS3 papers
- Centre Inria de l'Université de LilleFR2 papers
- Centre Inria de l'Université de RennesFR2 papers
- Max Planck SocietyDE2 papers
- University of California, BerkeleyUS2 papers
- University of PittsburghUS2 papers
48 papers
Evidence-based anomaly detection in clinical domains
Milos Hauskrecht, Michal Valko, Branislav Kveton +2
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that…
Learning from a single labeled face and a stream of unlabeled data
Branislav Kveton, Michal Valko
Face recognition from a single image per person is a challenging problem because the training sample is extremely small. We consider a variation of this problem. In our problem, we…
Conditional anomaly detection with soft harmonic functions
Michal Valko, Branislav Kveton, Hamed Valizadegan +2
In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-paramet…
Spectral bandits for smooth graph functions
Michal Valko, Rémi Munos, Branislav Kveton +1
Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs of arms are smooth on a graph…
CNN-based driving of block partitioning for intra slices encoding
Franck Galpin, Fabien Racapé, Sunil Jaiswal +3
This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in in…
StyleRig: Rigging StyleGAN for 3D Control over Portrait Images
Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj +5
StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face paramet…