1 code implementation • 29 Jun 2021 • Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman
We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use ranking statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data.
Ranked #1 on Novel Class Discovery on SVHN
1 code implementation • 7 Mar 2017 • Samuel Albanie, Sébastien Ehrhardt, João F. Henriques
While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed.
no code implementations • 6 Jun 2017 • Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra
While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters.
no code implementations • 26 May 2019 • Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra, Andrea Vedaldi
We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning.