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
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.
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.
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.